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1
Yost, Shawn, Márton Münz, Shazia Mahamdallie, Anthony Renwick, Elise Ruark, and Nazneen Rahman. "Clinical Annotation Reference Templates: a resource for consistent variant annotation." Wellcome Open Research 3 (November 14, 2018): 146. http://dx.doi.org/10.12688/wellcomeopenres.14924.1.
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Annotating the impact of a variant on a gene is a vital component of genetic medicine and genetic research. Different gene annotations for the same genomic variant are possible, because different structures and sequences for the same gene are available. The clinical community typically use RefSeq NMs to annotate gene variation, which do not always match the reference genome. The scientific community typically use Ensembl ENSTs to annotate gene variation. These match the reference genome, but often do not match the equivalent NM. Often the transcripts used to annotate gene variation are not provided, impeding interoperability and consistency. Here we introduce the concept of the Clinical Annotation Reference Template (CART). CARTs are analogous to the reference genome; they provide a universal standard template so reference genomic coordinates are consistently annotated at the protein level. Naturally, there are many situations where annotations using a specific transcript, or multiple transcripts are useful. The aim of the CARTs is not to impede this practice. Rather, the CART annotation serves as an anchor to ensure interoperability between different annotation systems and variant frequency accuracy. Annotations using other explicitly-named transcripts should also be provided, wherever useful. We have integrated transcript data to generate CARTs for over 18,000 genes, for both GRCh37 and GRCh38, based on the associated NM and ENST identified through the CART selection process. Each CART has a unique ID and can be used individually or as a stable set of templates; CART37A for GRCh37 and CART38A for GRCh38. We have made the CARTs available on the UCSC browser and in different file formats on the Open Science Framework: https://osf.io/tcvbq/. We have also made the CARTtools software we used to generate the CARTs available on GitHub. We hope the CARTs will be useful in helping to drive transparent, stable, consistent, interoperable variant annotation.
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Anderson, Matthew, Salman Sadiq, Muzammil Nahaboo Solim, Hannah Barker, David H. Steel, Maged Habib, and Boguslaw Obara. "Biomedical Data Annotation: An OCT Imaging Case Study." Journal of Ophthalmology 2023 (August 22, 2023): 1–9. http://dx.doi.org/10.1155/2023/5747010.
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In ophthalmology, optical coherence tomography (OCT) is a widely used imaging modality, allowing visualisation of the structures of the eye with objective and quantitative cross-sectional three-dimensional (3D) volumetric scans. Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. However, existing high-performance deep learning approaches rely on huge training datasets with high-quality annotations, which are challenging to obtain in many clinical applications. The collection of annotations from less experienced clinicians has the potential to alleviate time constraints from more senior clinicians, allowing faster data collection of medical image annotations; however, with less experience, there is the possibility of reduced annotation quality. In this study, we evaluate the quality of diabetic macular edema (DME) intraretinal fluid (IRF) biomarker image annotations on OCT B-scans from five clinicians with a range of experience. We also assess the effectiveness of annotating across multiple sessions following a training session led by an expert clinician. Our investigation shows a notable variance in annotation performance, with a correlation that depends on the clinician’s experience with OCT image interpretation of DME, and that having multiple annotation sessions has a limited effect on the annotation quality.
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Cronkite, David, Bradley Malin, John Aberdeen, Lynette Hirschman, and David Carrell. "Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification." Methods of Information in Medicine 55, no. 04 (2016): 356–64. http://dx.doi.org/10.3414/me15-01-0122.
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SummaryBackground: Clinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized.Objectives: This study models the costs and benefits of incorporating increasing numbers of independent human annotators to identify the instances of PII in a corpus. We used a corpus with gold standard annotations to evaluate the performance of teams of annotators of increasing size.Methods: Four annotators independently identified PII in a 100-document corpus consisting of randomly selected clinical notes from Family Practice clinics in a large integrated health care system. These annotations were pooled and validated to generate a gold standard corpus for evaluation.Results: Recall rates for all PII types ranged from 0.90 to 0.98 for individual annotators to 0.998 to 1.0 for teams of three, when measured against the gold standard. Median cost per PII instance discovered during corpus annotation ranged from $ 0.71 for an individual annotator to $ 377 for annotations discovered only by a fourth annotator.Conclusions: Incorporating a second annotator into a PII annotation process reduces unredacted PII and improves the quality of annotations to 0.99 recall, yielding clear benefit at reasonable cost; the cost advantages of annotation teams larger than two diminish rapidly.
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Park, Jimyung, Seng Chan You, Eugene Jeong, Chunhua Weng, Dongsu Park, Jin Roh, Dong Yun Lee, et al. "A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study." JMIR Medical Informatics 9, no. 3 (March 30, 2021): e23983. http://dx.doi.org/10.2196/23983.
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Background Although electronic health records (EHRs) have been widely used in secondary assessments, clinical documents are relatively less utilized owing to the lack of standardized clinical text frameworks across different institutions. Objective This study aimed to develop a framework for processing unstructured clinical documents of EHRs and integration with standardized structured data. Methods We developed a framework known as Staged Optimization of Curation, Regularization, and Annotation of clinical text (SOCRATex). SOCRATex has the following four aspects: (1) extracting clinical notes for the target population and preprocessing the data, (2) defining the annotation schema with a hierarchical structure, (3) performing document-level hierarchical annotation using the annotation schema, and (4) indexing annotations for a search engine system. To test the usability of the proposed framework, proof-of-concept studies were performed on EHRs. We defined three distinctive patient groups and extracted their clinical documents (ie, pathology reports, radiology reports, and admission notes). The documents were annotated and integrated into the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) database. The annotations were used for creating Cox proportional hazard models with different settings of clinical analyses to measure (1) all-cause mortality, (2) thyroid cancer recurrence, and (3) 30-day hospital readmission. Results Overall, 1055 clinical documents of 953 patients were extracted and annotated using the defined annotation schemas. The generated annotations were indexed into an unstructured textual data repository. Using the annotations of pathology reports, we identified that node metastasis and lymphovascular tumor invasion were associated with all-cause mortality among colon and rectum cancer patients (both P=.02). The other analyses involving measuring thyroid cancer recurrence using radiology reports and 30-day hospital readmission using admission notes in depressive disorder patients also showed results consistent with previous findings. Conclusions We propose a framework for hierarchical annotation of textual data and integration into a standardized OMOP-CDM medical database. The proof-of-concept studies demonstrated that our framework can effectively process and integrate diverse clinical documents with standardized structured data for clinical research.
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Yssel, Anna E. J., Shu-Min Kao, Yves Van de Peer, and Lieven Sterck. "ORCAE-AOCC: A Centralized Portal for the Annotation of African Orphan Crop Genomes." Genes 10, no. 12 (November 20, 2019): 950. http://dx.doi.org/10.3390/genes10120950.
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ORCAE (Online Resource for Community Annotation of Eukaryotes) is a public genome annotation curation resource. ORCAE-AOCC is a branch that is dedicated to the genomes published as part of the African Orphan Crops Consortium (AOCC). The motivation behind the development of the ORCAE platform was to create a knowledge-based website where the research-community can make contributions to improve genome annotations. All changes to any given gene-model or gene description are stored, and the entire annotation history can be retrieved. Genomes can either be set to “public” or “restricted” mode; anonymous users can browse public genomes but cannot make any changes. Aside from providing a user- friendly interface to view genome annotations, the platform also includes tools and information (such as gene expression evidence) that enables authorized users to edit and validate genome annotations. The ORCAE-AOCC platform will enable various stakeholders from around the world to coordinate their efforts to annotate and study underutilized crops.
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Keegan, Niamh M., Samantha E. Vasselman, Ethan Barnett, Barbara Nweji, Emily Carbone, Alexander Blum, Michael J. Morris, et al. "Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality." Journal of Clinical Oncology 40, no. 6_suppl (February 20, 2022): 64. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.064.
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64 Background: Routine clinical data from the electronic medical record are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. We sought to develop a prostate cancer-specific database with a defined source hierarchy for clinical annotations and to evaluate data reproducibility. Methods: At a comprehensive cancer center, we designed and implemented a clinical database for men with prostate cancer and clinical-grade paired tumor–normal sequencing for whom we performed team-based retrospective clinical data annotation from the electronic medical record, using a prostate cancer-specific data dictionary. We developed an open-source R package for data processing. We then evaluated completeness of data elements, reproducibility of team-based annotation using blinded repeat annotation by a medical oncologist as the reference, and the impact of measurement error on bias in survival analyses. Results: Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2,261 patients and their 2,631 genomically profiled samples. Completeness of data elements was generally high, between 55% to 99% for elements of clinical TNM staging, self-reported race, biopsy Gleason score, and presence of variant histologies, both for the team-based annotation and the repeat annotation. Comparing team-based annotation to the repeat annotation (100 patients/samples), reproducibility of annotations was high to very high. For 7 binary data elements, both sensitivity and specificity of the team-based annotation reached or exceeded 90%. The T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest. Conclusions: With a prostate cancer-specific data dictionary and quality control measures, manual team-based annotations can be scalable and reproducible. The data dictionary and the R package for reproducible data processing tools provided (https://stopsack.github.io/prostateredcap) are freely available to help increase data quality in clinical prostate cancer research.
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Moore, Jill E., Xiao-Ou Zhang, Shaimae I. Elhajjajy, Kaili Fan, Henry E. Pratt, Fairlie Reese, Ali Mortazavi, and Zhiping Weng. "Integration of high-resolution promoter profiling assays reveals novel, cell type–specific transcription start sites across 115 human cell and tissue types." Genome Research 32, no. 2 (December 23, 2021): 389–402. http://dx.doi.org/10.1101/gr.275723.121.
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Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated nor do they contain information on cell type–specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of approximately 50 thousand representative RAMPAGE peaks. These peaks are primarily proximal to GENCODE-annotated TSSs and are concordant with other transcription assays. Because RAMPAGE uses paired-end reads, we were then able to connect peaks to transcripts by analyzing the genomic positions of the 3′ ends of read mates. Using this paired-end information, we classified the vast majority (37 thousand) of our RAMPAGE peaks as verified TSSs, updating TSS annotations for 20% of GENCODE genes. We also found that these updated TSS annotations are supported by epigenomic and other transcriptomic data sets. To show the utility of this RAMPAGE rPeak collection, we intersected it with the NHGRI/EBI genome-wide association study (GWAS) catalog and identified new candidate GWAS genes. Overall, our work shows the importance of integrating experimental data to further refine TSS annotations and provides a valuable resource for the biological community.
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de Bruijn, Ino, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson, et al. "Abstract 1156: Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1156. http://dx.doi.org/10.1158/1538-7445.am2022-1156.
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Abstract Interpreting genomic variants in tumor samples presents a challenge in research and the clinical setting. A major barrier is that information about variants is fragmented across disparate databases, and aggregating information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one stop shop for variant annotation, equipped with a powerful API for bulk annotation of variants and a user friendly interface for cancer researchers. Genome Nexus is available at https://www.genomenexus.org. It a) aggregates variant information from a large number of sources that are relevant to cancer research and clinical applications; b) allows high-performance programmatic access to the aggregated data via a unified API; c) provides a search interface and a reference page for individual cancer variants; d) provides user-friendly tools for annotating variants in patients; e) is freely available under an open source license and can be installed in a private cloud or local environment. Genome Nexus contains annotations from more than a dozen resources, including those that provide variant effect information (VEP), protein sequence annotation (Uniprot, Pfam, dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, SIFT), population prevalence (gnomAD, dbSNP, ExAC), cancer population prevalence (Cancer Hotspots, SignalDB) and clinical actionability (OncoKB, CIViC, Clinvar). The annotations can be accessed through the website, the API, and a command line client. Genome Nexus is unique in providing a user friendly interface specific to cancer that allows high performance annotation of any variant. It is the main annotation service for the popular cancer genomics tool cBioPortal, which serves thousands of users daily. It is also offered as a standalone tool for annotation, allowing researchers and clinicians as well as genomic infrastructure developers to leverage it directly in their own workflows. For example, a local installation of Genome Nexus is used for annotating all variants in AACR Project GENIE. Citation Format: Ino de Bruijn, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson, Avery Wang, Hongxin Zhang, Aaron Lisman, Adam Abeshouse, Sander Rodenburg, Sjoerd van Hagen, Remond Fijneman, Gerrit Meijer, Nikolaus Schultz, Jianjiong Gao. Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1156.
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Queirós, Pedro, Polina Novikova, Paul Wilmes, and Patrick May. "Unification of functional annotation descriptions using text mining." Biological Chemistry 402, no. 8 (May 13, 2021): 983–90. http://dx.doi.org/10.1515/hsz-2021-0125.
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Abstract A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it is possible to identify highly similar functional descriptions, thus strengthening the confidence of the final protein functional annotation and providing a redundancy-free output. Here we present UniFunc, a text mining approach that is able to detect similar functional descriptions with high precision. UniFunc was built as a small module and can be independently used or integrated into protein function annotation pipelines. By removing the need to individually analyse and compare annotation results, UniFunc streamlines the complementary use of multiple reference datasets.
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Bax, Martin, Hilary Hart, and Sue Jenkins. "Annotations." Developmental Medicine & Child Neurology 23, no. 1 (November 12, 2008): 92–95. http://dx.doi.org/10.1111/j.1469-8749.1981.tb08450.x.
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Gedo, John E. "Annotations on Artemisia." Psychoanalytic Review 100, no. 5 (October 2013): 717–40. http://dx.doi.org/10.1521/prev.2013.100.5.717.
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Hinge, Kerry, Aditya Ghose, and Andrew Miller. "A Framework for Detecting Interactions Between Co-Incident Clinical Processes." International Journal of E-Health and Medical Communications 1, no. 2 (April 2010): 24–35. http://dx.doi.org/10.4018/jehmc.2010040103.
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The detection of treatment conflicts between multiple treatment protocols that are co-incident is a difficult and open problem that is particularly exacerbated regarding the treatment of multiple medical conditions co-occurring in aged patients. For example, a clinical protocol for prostate cancer treatment requires the administration of androgen-suppressing medication, which may negatively interact with another, co-incident protocol if the same patient were being treated for renal disease via haemodialysis, where androgen-enhancers are frequently administered. These treatment conflicts are subtle and difficult to detect using automated means. Traditional approaches to clinical decision support would require significant clinical knowledge. In this paper, the authors present an alternative approach that relies on encoding treatment protocols via process models (in BPMN) and annotating these models with semantic effect descriptions, which automatically detects conflicts. This paper describes an implemented tool (ProcessSEER) used for semantic effect annotation of a set of 12 cancer trial protocols and depicts the machinery required to detect treatment conflicts. The authors also argue whether the semantic effect annotations of treatment protocols can be leveraged for other tasks.
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Quick, Corbin, Xiaoquan Wen, Gonçalo Abecasis, Michael Boehnke, and Hyun Min Kang. "Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis." PLOS Genetics 16, no. 12 (December 15, 2020): e1009060. http://dx.doi.org/10.1371/journal.pgen.1009060.
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Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.
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Mei, Hao, Lianna Li, Fan Jiang, Jeannette Simino, Michael Griswold, Thomas Mosley, and Shijian Liu. "snpGeneSets: An R Package for Genome-Wide Study Annotation." G3 Genes|Genomes|Genetics 6, no. 12 (December 1, 2016): 4087–95. http://dx.doi.org/10.1534/g3.116.034694.
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Abstract Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.
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Lin, Jia-Wen, Feng Lu, Tai-Chen Lai, Jing Zou, Lin-Ling Guo, Zhi-Ming Lin, and Li Li. "Meibomian glands segmentation in infrared images with limited annotation." International Journal of Ophthalmology 17, no. 3 (March 18, 2024): 401–7. http://dx.doi.org/10.18240/ijo.2024.03.01.
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AIM: To investigate a pioneering framework for the segmentation of meibomian glands (MGs), using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis. METHODS: Totally 203 infrared meibomian images from 138 patients with dry eye disease, accompanied by corresponding annotations, were gathered for the study. A rectified scribble-supervised gland segmentation (RSSGS) model, incorporating temporal ensemble prediction, uncertainty estimation, and a transformation equivariance constraint, was introduced to address constraints imposed by limited supervision information inherent in scribble annotations. The viability and efficacy of the proposed model were assessed based on accuracy, intersection over union (IoU), and dice coefficient. RESULTS: Using manual labels as the gold standard, RSSGS demonstrated outcomes with an accuracy of 93.54%, a dice coefficient of 78.02%, and an IoU of 64.18%. Notably, these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%, 2.06%, and 2.69%, respectively. Furthermore, despite achieving a substantial 80% reduction in annotation costs, it only lags behind fully annotated methods by 0.72%, 1.51%, and 2.04%. CONCLUSION: An innovative automatic segmentation model is developed for MGs in infrared eyelid images, using scribble annotation for training. This model maintains an exceptionally high level of segmentation accuracy while substantially reducing training costs. It holds substantial utility for calculating clinical parameters, thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.
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Fan, Jung-wei, Jianrong Li, and Yves A. Lussier. "Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3818302.
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Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.
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Johnson, Amber, Yekaterina B. Khotskaya, Lauren Brusco, Jia Zeng, Vijaykumar Holla, Ann M. Bailey, Beate C. Litzenburger, et al. "Clinical Use of Precision Oncology Decision Support." JCO Precision Oncology, no. 1 (November 2017): 1–12. http://dx.doi.org/10.1200/po.17.00036.
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Purpose Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support team that provides annotations at the alteration level and subsequently determined whether clinical decision making was influenced. Methods Genomic alterations were annotated to determine actionability on the basis of a variant’s known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A Web-based survey was implemented to capture the reasons genotype-matched therapies were not pursued. Results The Precision Oncology Decision Support team processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. A total of 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially actionable, 29.7% unknown, and 28.4% nonactionable. Sixty-six percent of patients had at least one actionable/potentially actionable alteration, and 20.6% of patients (110 of 535) annotated enrolled in a genotype-matched trial. Trial enrollment was significantly higher for patients with actionable/potentially actionable alterations (92 of 333; 27.6%) than for those with unknown (16 of 136; 11.8%) and nonactionable (2 of 66; 3%) alterations ( P < .001). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment in genotype-matched trials. Clinicians cited a variety of reasons that patients with actionable alterations did not enroll in trials. Conclusion Over half of alterations annotated were of unknown significance or nonactionable. Physicians were more likely to enroll a patient in a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.
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Zhang, Jichang, Yuanjie Zheng, and Yunfeng Shi. "A Soft Label Method for Medical Image Segmentation with Multirater Annotations." Computational Intelligence and Neuroscience 2023 (February 18, 2023): 1–11. http://dx.doi.org/10.1155/2023/1883597.
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In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels’ learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater’s annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis.
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Luo, Yuan, and Peter Szolovits. "Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records." Biomedical Informatics Insights 8 (January 2016): BII.S38916. http://dx.doi.org/10.4137/bii.s38916.
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In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen's interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen's relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions.
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Sánchez-Salvador, Alejandro, Sandra González-de la Fuente, Begoña Aguado, Phillip A. Yates, and Jose M. Requena. "Refinement of Leishmania donovani Genome Annotations in the Light of Ribosome-Protected mRNAs Fragments (Ribo-Seq Data)." Genes 14, no. 8 (August 17, 2023): 1637. http://dx.doi.org/10.3390/genes14081637.
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Advances in next-generation sequencing methodologies have facilitated the assembly of an ever-increasing number of genomes. Gene annotations are typically conducted via specialized software, but the most accurate results require additional manual curation that incorporates insights derived from functional and bioinformatic analyses (e.g., transcriptomics, proteomics, and phylogenetics). In this study, we improved the annotation of the Leishmania donovani (strain HU3) genome using publicly available data from the deep sequencing of ribosome-protected mRNA fragments (Ribo-Seq). As a result of this analysis, we uncovered 70 previously non-annotated protein-coding genes and improved the annotation of around 600 genes. Additionally, we present evidence for small upstream open reading frames (uORFs) in a significant number of transcripts, indicating their potential role in the translational regulation of gene expression. The bioinformatics pipelines developed for these analyses can be used to improve the genome annotations of other organisms for which Ribo-Seq data are available. The improvements provided by these studies will bring us closer to the ultimate goal of a complete and accurately annotated L. donovani genome and will enhance future transcriptomics, proteomics, and genetics studies.
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Lin, Tai-Pei, Chiou-Ying Yang, Ko-Jiunn Liu, Meng-Yuan Huang, and Yen-Lin Chen. "Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma." Diagnostics 13, no. 24 (December 18, 2023): 3685. http://dx.doi.org/10.3390/diagnostics13243685.
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Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for annotation by non-pathologists and to develop an efficient model for distinguishing NPC without the time-consuming involvement of pathologists. For this study, we gathered NPC slides from 251 different patients, comprising hematoxylin and eosin (H&E) slides, pan-cytokeratin (Pan-CK) IHC slides, and Epstein–Barr virus-encoded small RNA (EBER) slides. The annotation of NPC regions in the H&E slides was carried out by a non-pathologist trainee who had access to corresponding Pan-CK IHC slides, both with and without EBER slides. The training process utilized ResNeXt, a deep neural network featuring a residual and inception architecture. In the validation set, NPC exhibited an AUC of 0.896, with a sensitivity of 0.919 and a specificity of 0.878. This study represents a significant breakthrough: the successful application of deep convolutional neural networks to identify NPC without the need for expert pathologist annotations. Our results underscore the potential of laboratory techniques to substantially reduce the workload of pathologists.
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Kleinert, Philip, and Martin Kircher. "A framework to score the effects of structural variants in health and disease." Genome Research 32, no. 4 (February 23, 2022): 766–77. http://dx.doi.org/10.1101/gr.275995.121.
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Although technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human- and chimpanzee-derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-deleterious, an approach that has proven powerful for short sequence variants. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as noncoding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV.
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Jaravine, Victor, James Balmford, Patrick Metzger, Melanie Boerries, Harald Binder, and Martin Boeker. "Annotation of Human Exome Gene Variants with Consensus Pathogenicity." Genes 11, no. 9 (September 14, 2020): 1076. http://dx.doi.org/10.3390/genes11091076.
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A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.
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Jo, Eunkyung, Rachael Zehrung, Katherine Genuario, Alexandra Papoutsaki, and Daniel A. Epstein. "Exploring Patient-Generated Annotations to Digital Clinical Symptom Measures for Patient-Centered Communication." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (November 7, 2024): 1–26. http://dx.doi.org/10.1145/3686997.
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Patients' self-reports are crucial for effective care management of clinical conditions involving subjective symptoms. While patients often value the ability to bring in different forms of self-report data to convey their lived experiences, they often struggle to make their data practically usable in clinical settings. To better center patient needs in communicating illness experiences in clinical contexts, we explore the idea of patient annotations to digital clinical self-report measures, specifically in the context of discontinuing antidepressants. Through interviews with 20 patients with AT Annotator, a digital aid to introduce the concept of annotations, we found that participants perceived annotations to digital clinical measures as a means to enrich self-report measures and reduce the cognitive and emotional burden of logging. However, concerns were raised regarding potential disruptions in patient-provider relationships and the sensitive and complex nature of mental health contexts. We discuss opportunities for annotations to promote patient-centered communication by balancing with clinical practicality and incorporating customization support for patients' communication needs.
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Zhang, Chao, Zhongwei Chen, Miming Zhang, and Shulei Jia. "KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs." Genes 14, no. 2 (February 1, 2023): 386. http://dx.doi.org/10.3390/genes14020386.
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The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for species-specific genes extraction and classification, which can output the results through an iterative keyword matching algorithm. It can not only extract and classify the amino acid sequences, but also the nucleotide sequences, and it has proved to be fast and efficient for microbial analysis. Analysis of the ancient Wood Ljungdahl (WL) pathway through the KEGG_Extractor reveals that ~226 archaeal strains contained the WL pathway-related genes. Most of them were Methanococcus maripaludis, Methanosarcina mazei and members of the Methanobacterium, Thermococcus and Methanosarcina genus. Using the KEGG_Extractor, the ARWL database was constructed, which had a high accuracy and complement. This tool helps to link genes with the KEGG pathway and promote the reconstruction of molecular networks. Availability and implementation: KEGG_Extractor is freely available from the GitHub.
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Lee, Kye Hwa, Hyunsung Lee, Jin-Hyeok Park, Yi-Jun Kim, and Youngho Lee. "ANNO: A General Annotation Tool for Bilingual Clinical Note Information Extraction." Healthcare Informatics Research 28, no. 1 (January 31, 2022): 89–94. http://dx.doi.org/10.4258/hir.2022.28.1.89.
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Objectives: This study was conducted to develop a generalizable annotation tool for bilingual complex clinical text annotation, which led to the design and development of a clinical text annotation tool, ANNO.Methods: We designed ANNO to enable human annotators to support the annotation of information in clinical documents efficiently and accurately. First, annotations for different classes (word or phrase types) can be tagged according to the type of word using the dictionary function. In addition, it is possible to evaluate and reconcile differences by comparing annotation results between human annotators. Moreover, if the regular expression set for each class is updated during annotation, it is automatically reflected in the new document. The regular expression set created by human annotators is designed such that a word tagged once is automatically labeled in new documents.Results: Because ANNO is a Docker-based web application, users can use it freely without being subjected to dependency issues. Human annotators can share their annotation markups as regular expression sets with a dictionary structure, and they can cross-check their annotated corpora with each other. The dictionary-based regular expression sharing function, cross-check function for each annotator, and standardized input (Microsoft Excel) and output (extensible markup language [XML]) formats are the main features of ANNO.Conclusions: With the growing need for massively annotated clinical data to support the development of machine learning models, we expect ANNO to be helpful to many researchers.
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Zhao, Zipei, Fengqian Pang, Yaou Liu, Zhiwen Liu, and Chuyang Ye. "Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations." Machine Learning for Biomedical Imaging 1, December 2022 (February 17, 2023): 1–30. http://dx.doi.org/10.59275/j.melba.2022-8g31.
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Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.
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Taleb, Aiham, Csaba Rohrer, Benjamin Bergner, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Christoph Lippert, and Joachim Krois. "Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification." Diagnostics 12, no. 5 (May 16, 2022): 1237. http://dx.doi.org/10.3390/diagnostics12051237.
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High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ≥45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive.
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Li, Dana, Lea Marie Pehrson, Rasmus Bonnevie, Marco Fraccaro, Jakob Thrane, Lea Tøttrup, Carsten Ammitzbøl Lauridsen, et al. "Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System." Diagnostics 13, no. 6 (March 11, 2023): 1070. http://dx.doi.org/10.3390/diagnostics13061070.
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A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered “gold standard”. Matthew’s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to “gold standard” (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.
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Chai, Yuan, Vincent Maes, A. Mounir Boudali, Brooke Rackel, and William L. Walter. "Inadequate Annotation and Its Impact on Pelvic Tilt Measurement in Clinical Practice." Journal of Clinical Medicine 13, no. 5 (February 28, 2024): 1394. http://dx.doi.org/10.3390/jcm13051394.
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Background: Accurate pre-surgical templating of the pelvic tilt (PT) angle is essential for hip and spine surgeries, yet the reliability of PT annotations is often compromised by human error, inherent subjectivity, and variations in radiographic quality. This study aims to identify challenges leading to inadequate annotations at a landmark dimension and evaluating their impact on PT. Methods: We retrospectively collected 115 consecutive sagittal radiographs for the measurement of PT based on two definitions: the anterior pelvic plane and a line connecting the femoral head’s centre to the sacral plate’s midpoint. Five annotators engaged in the measurement, followed by a secondary review to assess the adequacy of the annotations across all the annotators. Results: The outcomes indicated that over 60% images had at least one landmark considered inadequate by the majority of the reviewers, with poor image quality, outliers, and unrecognized anomalies being the primary causes. Such inadequacies led to discrepancies in the PT measurements, ranging from −2° to 2°. Conclusion: This study highlights that landmarks annotated from clear anatomical references were more reliable than those estimated. It also underscores the prevalence of suboptimal annotations in PT measurements, which extends beyond the scope of traditional statistical analysis and could result in significant deviations in individual cases, potentially impacting clinical outcomes.
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Reynolds, Regina H., John Hardy, Mina Ryten, and Sarah A. Gagliano Taliun. "Informing disease modelling with brain-relevant functional genomic annotations." Brain 142, no. 12 (October 11, 2019): 3694–712. http://dx.doi.org/10.1093/brain/awz295.
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How can we best translate the success of genome-wide association studies for neurological and neuropsychiatric diseases into therapeutic targets? Reynolds et al. critically assess existing brain-relevant functional genomic annotations and the tools available for integrating such annotations with summary-level genetic association data.
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Ghiasvand, Omid, and Rohit J. Kate. "Learning for clinical named entity recognition without manual annotations." Informatics in Medicine Unlocked 13 (2018): 122–27. http://dx.doi.org/10.1016/j.imu.2018.10.011.
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Cary, Michael, Katie Podshivalova, and Cynthia Kenyon. "Application of Transcriptional Gene Modules to Analysis of Caenorhabditis elegans’ Gene Expression Data." G3: Genes|Genomes|Genetics 10, no. 10 (August 5, 2020): 3623–38. http://dx.doi.org/10.1534/g3.120.401270.
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Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism C. elegans. These modules represent responses to changes in the environment (e.g., starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g., ATFS-1, DAF-16), genes specific to tissues (e.g., neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g., GO term). Analysis of “module-weighted annotations” improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource with tutorials at http://genemodules.org/, in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own gene expression data (generated using any platform) or gene sets of interest.
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Rahm, Erhard, Toralf Kirsten, and Jörg Lange. "The GeWare data warehouse platform for the analysis of molecular-biological and clinical data." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 1–11. http://dx.doi.org/10.1515/jib-2007-47.
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Abstract We introduce the GeWare data warehouse platform for the integrated analysis of clinical information, microarray data and annotations within large biomedical research studies. Clinical data is obtained from a commercial study management system while publicly available data is integrated using a mediator approach. The platform utilizes a generic approach to manage different types of annotations. We outline the overall architecture of the platform, its implementation as well as the main processing and analysis workflows.
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Vieira, Alexandre R. "Multiple annotations forGCPII in the htgs database." American Journal of Medical Genetics 123A, no. 3 (November 3, 2003): 316. http://dx.doi.org/10.1002/ajmg.a.20337.
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Philipp, Markus, Anna Alperovich, Alexander Lisogorov, Marielena Gutt-Will, Andrea Mathis, Stefan Saur, Andreas Raabe, and Franziska Mathis-Ullrich. "Annotation-efficient learning of surgical instrument activity in neurosurgery." Current Directions in Biomedical Engineering 8, no. 1 (July 1, 2022): 30–33. http://dx.doi.org/10.1515/cdbme-2022-0008.
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Abstract Machine learning-based solutions rely heavily on the quality and quantity of the training data. In the medical domain, the main challenge is to acquire rich and diverse annotated datasets for training. We propose to decrease the annotation efforts and further diversify the dataset by introducing an annotation-efficient learning workflow. Instead of costly pixel-level annotation, we require only image-level labels as the remainder is covered by simulation. Thus, we obtain a large-scale dataset with realistic images and accurate ground truth annotations. We use this dataset for the instrument localization activity task together with a studentteacher approach. We demonstrate the benefits of our workflow compared to state-of-the-art methods in instrument localization that are trained only on clinical datasets, which are fully annotated by human experts.
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Di Bartolomeo, Mattia, Arrigo Pellacani, Federico Bolelli, Marco Cipriano, Luca Lumetti, Sara Negrello, Stefano Allegretti, et al. "Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm." Applied Sciences 13, no. 5 (March 3, 2023): 3271. http://dx.doi.org/10.3390/app13053271.
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Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.
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Mrowiec, Thomas, Sharon Ruane, Simon Schallenberg, Gabriel Dernbach, Rumyana Todorova, Cornelius Böhm, Walter de Back, et al. "Abstract 457: Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples." Cancer Research 82, no. 12_Supplement (June 15, 2022): 457. http://dx.doi.org/10.1158/1538-7445.am2022-457.
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Abstract Background: Automated cell-level characterization of the tumor microenvironment (TME) at scale is key to data-driven immuno-oncology. Artificial intelligence (AI)-powered analysis of hematoxylin and eosin (H&E) images scales and has recently been translated into diagnostics. However, robust TME analysis solely based on H&E data is bound by the stain's properties and by manual pathologist annotations, both in number and accuracy. In this study, we quantify the error introduced by pathologists' morphological assessment and mitigate this error by training AI-systems without manual pathologist annotations, using labels determined directly from IHC profiles. Methods: The work was carried out on 239 clinical NSCLC cases. CK-KL1, CD3+CD20, and Mum1 were used for defining carcinoma (CA), lymphocyte (LY), and plasma (PL) cells. For evaluation, representative regions were annotated by 3 trained pathologists. The workflow is based on co-registration of same-section H&E and IHC stained images with single cell precision. Cells were detected in H&E and labelled using rule-based algorithms that incorporated IHC information. This H&E data was used to train neural networks (NN). Results: (A) The inter-rater agreement of pathologists annotating on H&E is increased when information from registered IHC images is provided. (B) The concordance of pathologists on H&E-only compared to on H&E+IHC shows that pathologists miss or misclassify cells with a certain error. (C) NNs trained with IHC-based labels achieve similar performance for cell type classification on H&E as pathologists on H&E. Conclusion: This study demonstrates the value of combining histomorphological and IHC data for improved cell annotation. Our novel workflow provides a quantitative benchmark and facilitates training of accurate AI models for quantitative characterization of tumor and TME from H&E sections. A) Inter-rater agreement by metric, stain, and cell type By cell count, Pearson correlation By cell count, Pearson correlation By cell location, Krippendorff’s alpha By cell location, Krippendorff’s alpha Cell type H&E-only H&E+IHC H&E-only H&E+IHC CA 0.86 0.98 0.43 0.90 LY 0.88 0.99 0.21 0.76 PL 0.77 0.96 0.32 0.87 B) Performance of individual pathologists in H&E Against consensus in H&E+IHC Against own annotations in H&E+IHC Against own annotations in H&E+IHC Cell type By cell count, Pearson correlation By cell location, Precision By cell location, Recall CA 0.84 0.76 0.77 LY 0.78 0.70 0.60 PL 0.76 0.69 0.21 C) NN against annotator H&E+IHC consensus Cell Type By cell count, Pearson correlation CA 0.84 LY 0.92 PL 0.75 Citation Format: Thomas Mrowiec, Sharon Ruane, Simon Schallenberg, Gabriel Dernbach, Rumyana Todorova, Cornelius Böhm, Walter de Back, Blanca Pablos, Roman Schulte-Sasse, Ivana Trajanovska, Adelaida Creosteanu, Emil Barbuta, Marcus Otte, Christian Ihling, Hans Juergen Grote, Juergen Scheuenpflug, Viktor Matyas, Maximilian Alber, Frederick Klauschen. Immunohistochemistry-informed AI systems for improved characterization of tumor-microenvironment in clinical non-small cell lung cancer H&E samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 457.
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Schilling, Marcel P., Niket Ahuja, Luca Rettenberger, Tim Scherr, and Markus Reischl. "Impact of Annotation Noise on Histopathology Nucleus Segmentation." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 197–200. http://dx.doi.org/10.1515/cdbme-2022-1051.
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Abstract Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a concept to categorize and simulate various types of annotation noise as well as an evaluation of the impact on deep learning pipelines. Thereby, we use the multi-organ histology image dataset MoNuSeg to discuss the influence of annotator variability. We provide annotation recommendations for clinicians to achieve high-quality automated diagnostic algorithms.
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Siadjeu, Christian, Boas Pucker, Prisca Viehöver, Dirk C. Albach, and Bernd Weisshaar. "High Contiguity de novo Genome Sequence Assembly of Trifoliate Yam (Dioscorea dumetorum) Using Long Read Sequencing." Genes 11, no. 3 (March 4, 2020): 274. http://dx.doi.org/10.3390/genes11030274.
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Trifoliate yam (Dioscorea dumetorum) is one example of an orphan crop, not traded internationally. Post-harvest hardening of the tubers of this species starts within 24 h after harvesting and renders the tubers inedible. Genomic resources are required for D. dumetorum to improve breeding for non-hardening varieties as well as for other traits. We sequenced the D. dumetorum genome and generated the corresponding annotation. The two haplophases of this highly heterozygous genome were separated to a large extent. The assembly represents 485 Mbp of the genome with an N50 of over 3.2 Mbp. A total of 35,269 protein-encoding gene models as well as 9941 non-coding RNA genes were predicted, and functional annotations were assigned.
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Albright, Daniel, Arrick Lanfranchi, Anwen Fredriksen, William F. Styler, Colin Warner, Jena D. Hwang, Jinho D. Choi, et al. "Towards comprehensive syntactic and semantic annotations of the clinical narrative." Journal of the American Medical Informatics Association 20, no. 5 (September 2013): 922–30. http://dx.doi.org/10.1136/amiajnl-2012-001317.
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Myers, Florence L. "Annotations of research and clinical perspectives on cluttering since 1964." Journal of Fluency Disorders 21, no. 3-4 (September 1996): 187–99. http://dx.doi.org/10.1016/s0094-730x(96)00022-8.
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Rosano-Gonzalez, María L., Vipin T. Sreedharan, Antoine Hanns, Daniel J. Stekhoven, and Franziska Singer. "CIViCutils: Matching and downstream processing of clinical annotations from CIViC." F1000Research 12 (October 11, 2023): 1304. http://dx.doi.org/10.12688/f1000research.136986.1.
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Background: With the advent of next-generation sequencing, profiling the genetic landscape of tumors entered clinical diagnostics, bringing the resolution of precision oncology to unprecedented levels. However, the wealth of information generated in a sequencing experiment can be difficult to manage, especially if hundreds of mutations need to be interpreted in a clinical context. Dedicated methods and databases are required that assist in interpreting the importance of a mutation for disease progression, prognosis, and with respect to therapy. Here, the CIViC knowledgebase is a valuable curated resource, however, utilizing CIViC in an efficient way for querying a large number of mutations needs sophisticated downstream methods. Methods: To this end, we have developed CIViCutils, a Python package to query, annotate, prioritize, and summarize information from the CIViC database. Our package provides functionality for performing high-throughput searches in CIViC, automatically matching clinical evidence to input variants, evaluating the accuracy of the extracted variant matches, fully exploiting the available disease-specific information according to cancer types of interest, and in-silico predicting drug-target interactions tailored to individual patients. Results: CIViCutils allows the simultaneous query of hundreds of mutations and is able to harmonize input across different nomenclatures. Moreover, it supports gene expression data, single nucleotide mutations, as well as copy number alterations as input. We utilized CIViCutils in a study on the bladder cancer cohort from The Cancer Genome Atlas (TCGA-BLCA), where it helped to extract clinically relevant mutations for personalized therapy recommendation. Conclusions: CIViCutils is an easy-to-use Python package that can be integrated into workflows for profiling the genetic landscape of tumor samples. It streamlines interpreting large numbers of variants with retrieving and processing curated CIViC information.
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Milchevskaya, Vladislava, Grischa Tödt, and Toby James Gibson. "A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays." Genomics and Computational Biology 3, no. 2 (January 31, 2017): 38. http://dx.doi.org/10.18547/gcb.2017.vol3.iss2.e38.
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Genome-wide expression profiling and genotyping is widely applied in functional genomics research, ranging from stem cell studies to cancer, in drug response studies, and in clinical diagnostics. The Affymetrix GeneChip microarrays represent the most popular platform for such assays. Nevertheless, due to rapid and continuous improvement of the knowledge about the genome, the definition of many of the genes and transcripts change, and new genes are discovered. Thus the original probe information is out-dated for a number of Affymetrix platforms, and needs to be re-defined. It has been demonstrated, that accurate probe set definition improves both coverage of the gene expression analysis and its statistical power. Therefore we developed a method that incorporates the most recent genome annotations into the annotation of the microarray probe sets, using tools from the next generation sequencing. Additionally our method allows to quickly build project specific gene annotation models, as well as for comparison of microarray to RNAseq data.
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Sarma, Karthik V., Alex G. Raman, Nikhil J. Dhinagar, Alan M. Priester, Stephanie Harmon, Thomas Sanford, Sherif Mehralivand, et al. "Harnessing clinical annotations to improve deep learning performance in prostate segmentation." PLOS ONE 16, no. 6 (June 25, 2021): e0253829. http://dx.doi.org/10.1371/journal.pone.0253829.
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Purpose Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets. Materials and methods We used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset. Results Our model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data. Conclusion We trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.
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Coetzee, Simon G., Zachary Ramjan, Huy Q. Dinh, Benjamin P. Berman, and Dennis J. Hazelett. "StateHub-StatePaintR: rapid and reproducible chromatin state evaluation for custom genome annotation." F1000Research 7 (February 22, 2018): 214. http://dx.doi.org/10.12688/f1000research.13535.1.
Full textAbstract:
Genome annotation is critical to understand the function of disease variants, especially for clinical applications. To meet this need there are segmentations available from public consortia reflecting varying unsupervised approaches to functional annotation based on epigenetics data, but there remains a need for transparent, reproducible, and easily interpreted genomic maps of the functional biology of chromatin. We introduce a new methodological framework for defining a combinatorial epigenomic model of chromatin state on a web database, StateHub. In addition, we created an annotation tool for bioconductor, StatePaintR, which accesses these models and uses them to rapidly (on the order of seconds) produce chromatin state segmentations in standard genome browser formats. Annotations are fully documented with change history and versioning, authorship information, and original source files. StatePaintR calculates ranks for each state from next-gen sequencing peak statistics, facilitating variant prioritization, enrichment testing, and other types of quantitative analysis. StateHub hosts annotation tracks for major public consortia as a resource, and allows users to submit their own alternative models.
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Coetzee, Simon G., Zachary Ramjan, Huy Q. Dinh, Benjamin P. Berman, and Dennis J. Hazelett. "StateHub-StatePaintR: rapid and reproducible chromatin state evaluation for custom genome annotation." F1000Research 7 (May 7, 2020): 214. http://dx.doi.org/10.12688/f1000research.13535.2.
Full textAbstract:
Genome annotation is critical to understand the function of disease variants, especially for clinical applications. To meet this need there are segmentations available from public consortia reflecting varying unsupervised approaches to functional annotation based on epigenetics data, but there remains a need for transparent, reproducible, and easily interpreted genomic maps of the functional biology of chromatin. We introduce a new methodological framework for defining a combinatorial epigenomic model of chromatin state on a web database, StateHub. In addition, we created an annotation tool for bioconductor, StatePaintR, which accesses these models and uses them to rapidly (on the order of seconds) produce chromatin state segmentations in standard genome browser formats. Annotations are fully documented with change history and versioning, authorship information, and original source files. StatePaintR calculates ranks for each state from next-gen sequencing peak statistics, facilitating variant prioritization, enrichment testing, and other types of quantitative analysis. StateHub hosts annotation tracks for major public consortia as a resource, and allows users to submit their own alternative models.
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Hernandez, Luis Alberto Robles, Tiffany J. Callahan, and Juan M. Banda. "A biomedically oriented automatically annotated Twitter COVID-19 dataset." Genomics & Informatics 19, no. 3 (September 30, 2021): e21. http://dx.doi.org/10.5808/gi.21011.
Full textAbstract:
The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don’t generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.
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Santiago-Rodriguez, Tasha M., Aaron Garoutte, Emmase Adams, Waleed Nasser, Matthew C. Ross, Alex La Reau, Zachariah Henseler, et al. "Metagenomic Information Recovery from Human Stool Samples Is Influenced by Sequencing Depth and Profiling Method." Genes 11, no. 11 (November 21, 2020): 1380. http://dx.doi.org/10.3390/genes11111380.
Full textAbstract:
Sequencing of the 16S rRNA gene (16S) has long been a go-to method for microbiome characterization due to its accessibility and lower cost compared to shotgun metagenomic sequencing (SMS). However, 16S sequencing rarely provides species-level resolution and cannot provide direct assessment of other taxa (e.g., viruses and fungi) or functional gene content. Shallow shotgun metagenomic sequencing (SSMS) has emerged as an approach to bridge the gap between 16S sequencing and deep metagenomic sequencing. SSMS is cost-competitive with 16S sequencing, while also providing species-level resolution and functional gene content insights. In the present study, we evaluated the effects of sequencing depth on marker gene-mapping- and alignment-based annotation of bacteria in healthy human stool samples. The number of identified taxa decreased with lower sequencing depths, particularly with the marker gene-mapping-based approach. Other annotations, including viruses and pathways, also showed a depth-dependent effect on feature recovery. These results refine the understanding of the suitability and shortcomings of SSMS, as well as annotation tools for metagenomic analyses in human stool samples. Results may also translate to other sample types and may open the opportunity to explore the effect of sequencing depth and annotation method.
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Mendieta, John Pablo, Alexandre P. Marand, William A. Ricci, Xuan Zhang, and Robert J. Schmitz. "Leveraging histone modifications to improve genome annotations." G3 Genes|Genomes|Genetics, July 27, 2021. http://dx.doi.org/10.1093/g3journal/jkab263.
Full textAbstract:
Abstract Accurate genome annotations are essential to modern biology; however, they remain challenging to produce. Variation in gene structure and expression across species, as well as within an organism, make correctly annotating genes arduous; an issue exacerbated by pitfalls in current in silico methods. These issues necessitate complementary approaches to add additional confidence and rectify potential misannotations. Integration of epigenomic data into genome annotation is one such approach. In this study, we utilized sets of histone modification data, which are precisely distributed at either gene bodies or promoters to evaluate the annotation of the Zea mays genome. We leveraged these data genome wide, allowing for identification of annotations discordant with empirical data. In total, 13,159 annotation discrepancies were found in Z. mays upon integrating data across three different tissues, which were corroborated using RNA-based approaches. Upon correction, genes were extended by an average of 2128 base pairs, and we identified 2529 novel genes. Application of this method to five additional plant genomes identified a series of misannotations, as well as identified novel genes, including 13,836 in Asparagus officinalis, 2724 in Setaria viridis, 2446 in Sorghum bicolor, 8631 in Glycine max, and 2585 in Phaseolous vulgaris. This study demonstrates that histone modification data can be leveraged to rapidly improve current genome annotations across diverse plant lineages.