Relevant bibliographies by topics / ACMG classification

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  2. Dissertations / Theses
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Academic literature on the topic 'ACMG classification'

Author: Grafiati
Published: 21 December 2024

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Journal articles on the topic "ACMG classification"

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Lugeiro, Palloma C., Betsaida Urtremari, Lucas S. Santana, Elisangela P. S. Quedas, and Delmar Muniz Lourenco. "Comparative Analysis of Different International Criteria (ACMG-AMP vs. TENGEN) Applied to Classification of Missense Germline Allelic Variants in Patients With Multiple Endocrine Neoplasia Type 1 or Suspected to this Syndrome." Journal of the Endocrine Society 5, Supplement_1 (May 1, 2021): A1014. http://dx.doi.org/10.1210/jendso/bvab048.2074.

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Abstract Context: Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominant genetic syndrome caused by germline pathogenic allele variants (PAV) in the MEN1 tumor suppressor gene, which predispose MEN1 carriers to the increased risk of several endocrine neoplasms throughout life. The MEN1 gene (11q13), contains 10 exons encoding the MENIN protein. About 600 different PAVs have been reported, with 25% of them being missense variants. Of value, the definition of pathogenicity can be challenging, especially for missense variants. Thus, international guidelines for improving the classification of allele variants were recently defined by the ACMG-AMP (2015). Recently, applying ACMG-AMP criteria with inclusion of clinical features the TENGEN French group suggested modifications aiming to refine the classification of variants in MEN1 syndrome. Objective: To classify missense allelic variants found in the MEN1 gene by the ACMG-AMP guideline using VARSOME and by the TENGEN group to support a comparative analysis of the results obtained with these two methodologies (ACMG-AMP; TENGEN). Methods: the classification of 16 different missense allele variants identified in 17 index cases with or suspected to MEN1 syndrome was conducted according to ACMG-AMP criteria using the VARSOME software followed by the analysis defined by the TENGEN group. Results: Of the 16 variants, 6 were new, 1 was recurrent (2 unrelated index cases) and 9 of them occurred in codons with previous reports of different amino acid exchanges in the same region. Differences observed in the classification by ACMG-AMP and TENGEN were: pathogenic variant (6% vs. 65%); probably pathogenic (88% vs. 12%) and variants of uncertain significance (VUS) (6% vs. 23%). The four VUS classified by TENGEN (one of them for ACMG-AMP) were of sporadic cases without clinical diagnosis of MEN1 (2, for one MEN1-related tumor in early age; 1, for suspected MEN1) or with high risk of phenocopy (1, HPT + acromegaly). Conclusion: The difference observed in the classification of the pathogenicity of these variants, especially due to the higher occurrence of VUS in TENGEN, indicates that the criteria adopted by ACMG-VARSOME would have to be refined for clinical features. By other side, TENGEN apparently reinforce the classification of pathogenicity in cases with clinical diagnosis of MEN1 and reduce the definition of pathogenicity to variants found in MEN1-suspected cases without clinical criteria for the MEN1 diagnosis. These protocols apparently need to be investigate, validated and, probably, improved in other cohorts to reduce risks of misinterpretations and classifications that can, lately, interfere in genetic counseling and in the clinical management of patients. Finally, long-term outcome of cases classified as VUS, functional studies and, familial segregation may reinforce the initial impressions obtained with TENGEN classification.
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Cristofoli, Francesca, Muharrem Daja, Paolo Enrico Maltese, Giulia Guerri, Benedetta Tanzi, Roberta Miotto, Gabriele Bonetti, et al. "MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations." Genes 14, no. 8 (August 8, 2023): 1600. http://dx.doi.org/10.3390/genes14081600.

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We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.
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Mattivi, Connor L., J. Martijn Bos, Richard D. Bagnall, Natalie Nowak, John R. Giudicessi, Steve R. Ommen, Christopher Semsarian, and Michael J. Ackerman. "Clinical Utility of a Phenotype-Enhanced MYH7 -Specific Variant Classification Framework in Hypertrophic Cardiomyopathy Genetic Testing." Circulation: Genomic and Precision Medicine 13, no. 5 (October 2020): 453–59. http://dx.doi.org/10.1161/circgen.120.003039.

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Background: Missense variants in the MYH7 -encoded MYH7 (beta myosin heavy chain 7) represent a leading cause of hypertrophic cardiomyopathy (HCM). MYH7 -specific American College of Medical Genetics and Genomics (ACMG) variant classification guidelines were released recently but have yet to be assessed independently. We set out to assess the performance of the MYH7 -specific ACMG guidelines and determine if the addition of phenotype-enhanced criteria (PE-ACMG) using the HCM Genotype Predictor Score can further reduce the burden of variants of uncertain significance (VUS). Methods: Re-assessment was performed on 70 MYH7 -variants in 121 unique patients from Mayo Clinic, and an independent cohort of 54 variants in 70 patients from Royal Prince Alfred Hospital (Australia). Qualifying variants were re-adjudicated using both standard ACMG and MYH7 -ACMG guidelines, and HCM Genotype Predictor Score was used to provide a validated measure of strength of clinical phenotype to be incorporated into the MYH7 -ACMG framework. Results: Among Mayo Clinic identified variants, 11/70 (16%) were classified as pathogenic (P), 10/70 (14%) as likely pathogenic, and 49/70 (70%) as a VUS. A similar distribution was seen in the Australian patients (12/54 [22%] P, 12/54 [22%] likely pathogenic, and 30/54 [56%] VUS; P =not significant). Application of the MYH7 -ACMG resulted in a nonsignificant reduction of the VUS burden in both cohorts from 49/70 to 39/70 (56%; P =0.1; Mayo Clinic) and from 30/54 to 20/54 (37%; P =0.1; Australia). Using the combined PE-MYH7-ACMG framework, the VUS decreased significantly from 49 to 27 ( P <0.001, Mayo Clinic) and from 30 to 16 ( P <0.001; Australia). Conclusions: Use of the MYH7 -specific guidelines alone failed to significantly decrease VUS burden in 2 independent cohorts. However, a significant reduction in VUS burden was observed after the addition of phenotypic criteria. Using a patient’s strength of sarcomeric HCM phenotype for variant adjudication can increase significantly the clinical utility of genetic testing for patients with HCM.
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Cheng, Liting, Xiaoyan Li, Lin Zhao, Zefeng Wang, Junmeng Zhang, Zhuo Liang, and Yongquan Wu. "Reevaluating the Mutation Classification in Genetic Studies of Bradycardia Using ACMG/AMP Variant Classification Framework." International Journal of Genomics 2020 (February 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/2415850.

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Purpose. Next-generation sequencing (NGS) has become more accessible, leading to an increasing number of genetic studies of familial bradycardia being reported. However, most of the variants lack full evaluation. The relationship between genetic factors and bradycardia should be summarized and reevaluated. Methods. We summarized genetic studies published in the PubMed database from 2008/1/1 to 2019/9/1 and used the ACMG/AMP classification framework to analyze related sequence variants. Results. We identified 88 articles, 99 sequence variants, and 34 genes after searching the PubMed database and classified ABCC9, ACTN2, CACNA1C, DES, HCN4, KCNQ1, KCNH2, LMNA, MECP2, LAMP2, NPPA, SCN5A, and TRPM4 as high-priority genes causing familial bradycardia. Most mutated genes have been reported as having multiple clinical manifestations. Conclusions. For patients with familial CCD, 13 high-priority genes are recommended for evaluation. For genetic studies, variants should be carefully evaluated using the ACMG/AMP variant classification framework before publication.
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Brown, Angela, Mansour Zamanpoor, Donald R. Love, and Debra O. Prosser. "Determination of Pathogenicity of Breast Cancer 1 Gene Variants using the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Guidelines." Sultan Qaboos University Medical Journal [SQUMJ] 19, no. 4 (December 22, 2019): 324. http://dx.doi.org/10.18295/squmj.2019.19.04.008.

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Objectives: Molecular diagnostic laboratories screen for mutations in disease-causing genes in order to confirm a clinical diagnosis. The classification of DNA variants as ‘pathogenic’ or ‘likely pathogenic’ mutations creates a workflow bottleneck, which becomes increasingly challenging as greater number of genes are screened. The classification challenge is also acute if there are conflicting reports regarding pathogenicity and differing classification criteria between laboratories. This study aimed to compare two procedures for the classification of variants in the breast cancer (BRCA)1 gene. Methods: This bioinformatic study was conducted at LabPLUS, Auckland, New Zealand, from February to June 2017. DNA was extracted from peripheral blood samples of 30 patients and gene library construction was carried out using a commercially available targeted panel for the BRCA1 and BRCA2 genes. The genes were subsequently sequenced and the sequence data analysed. The guidelines published by the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/ AMP) provides a comprehensive framework for the interpretation of variants in genes that are associated with Mendelian disorders. The use of these guidelines were compared to the variant classifications that were achieved by reference to those reported in the BRCA Exchange database. Results: The results showed concordance between the two classification protocols for a panel of 30 BRCA1 gene variants, although the transparency in following the ACMG/AMP guidelines provides a diagnostic laboratory with a generalisable approach that allows laboratorydirected revisions to be undertaken in light of new information. Conclusion: The ACMG/AMP-based guidelines were applied to a cohort of patients with BRCA1 gene variants. The use of these guidelines provides a system which creates consistency in variant interpretation and supports subsequent clinical management.Keywords: BRCA1 Gene; Bioinformatics; DNA Sequencing; Nonsense Codon; Splice Donor Site; New Zealand.
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Cristofoli, Francesca, Elisa Sorrentino, Giulia Guerri, Roberta Miotto, Roberta Romanelli, Alessandra Zulian, Stefano Cecchin, et al. "Variant Selection and Interpretation: An Example of Modified VarSome Classifier of ACMG Guidelines in the Diagnostic Setting." Genes 12, no. 12 (November 25, 2021): 1885. http://dx.doi.org/10.3390/genes12121885.

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Variant interpretation is challenging as it involves combining different levels of evidence in order to evaluate the role of a specific variant in the context of a patient’s disease. Many in-depth refinements followed the original 2015 American College of Medical Genetics (ACMG) guidelines to overcome subjective interpretation of criteria and classification inconsistencies. Here, we developed an ACMG-based classifier that retrieves information for variant interpretation from the VarSome Stable-API environment and allows molecular geneticists involved in clinical reporting to introduce the necessary changes to criterion strength and to add or exclude criteria assigned automatically, ultimately leading to the final variant classification. We also developed a modified ACMG checklist to assist molecular geneticists in adjusting criterion strength and in adding literature-retrieved or patient-specific information, when available. The proposed classifier is an example of integration of automation and human expertise in variant curation, while maintaining the laboratory analytical workflow and the established bioinformatics pipeline.
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Liu, Yichuan, Hui-Qi Qu, Adam S. Wenocur, Jingchun Qu, Xiao Chang, Joseph Glessner, Patrick Sleiman, Lifeng Tian, and Hakon Hakonarson. "Interpretation of Maturity-Onset Diabetes of the Young Genetic Variants Based on American College of Medical Genetics and Genomics Criteria: Machine-Learning Model Development." JMIR Biomedical Engineering 5, no. 1 (December 1, 2020): e20506. http://dx.doi.org/10.2196/20506.

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Background Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY, and HNF1A-MODY as the three most common forms based on the causal genes. Molecular diagnosis of MODY is important for precise treatment. Although a DNA variant causing MODY can be assessed based on the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate among MODY subtypes. As the ACMG criteria were not originally designed for machine-learning algorithms, they are not true independent variables. Objective The aim of this study was to develop machine-learning models for interpretation of DNA variants and MODY diagnosis using the ACMG criteria. Methods We applied machine-learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on the Human Gene Mutation Database (HGMD) and ClinVar database. Results With a machine-learning procedure, we found that the weight matrix of the ACMG criteria was significantly different between the three MODY genes HNF1A, HNF4A, and GCK. The models showed high predictive abilities with accuracy over 95%. Conclusions Our results highlight the need for applying different weights of the ACMG criteria in relation to different MODY genes for accurate functional classification. As proof of principle, we applied the ACMG criteria as feature vectors in a machine-learning model and obtained a precision-based result.
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Tavtigian, Sean V., Marc S. Greenblatt, Steven M. Harrison, Robert L. Nussbaum, Snehit A. Prabhu, Kenneth M. Boucher, and Leslie G. Biesecker. "Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework." Genetics in Medicine 20, no. 9 (January 4, 2018): 1054–60. http://dx.doi.org/10.1038/gim.2017.210.

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Lattante, Serena, Giuseppe Marangi, Paolo Niccolò Doronzio, Amelia Conte, Giulia Bisogni, Marcella Zollino, and Mario Sabatelli. "High-Throughput Genetic Testing in ALS: The Challenging Path of Variant Classification Considering the ACMG Guidelines." Genes 11, no. 10 (September 24, 2020): 1123. http://dx.doi.org/10.3390/genes11101123.

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The development of high-throughput sequencing technologies and screening of big patient cohorts with familial and sporadic amyotrophic lateral sclerosis (ALS) led to the identification of a significant number of genetic variants, which are sometimes difficult to interpret. The American College of Medical Genetics and Genomics (ACMG) provided guidelines to help molecular geneticists and pathologists to interpret variants found in laboratory testing. We assessed the application of the ACMG criteria to ALS-related variants, combining data from literature with our experience. We analyzed a cohort of 498 ALS patients using massive parallel sequencing of ALS-associated genes and identified 280 variants with a minor allele frequency < 1%. Examining all variants using the ACMG criteria, thus considering the type of variant, inheritance, familial segregation, and possible functional studies, we classified 20 variants as “pathogenic”. In conclusion, ALS’s genetic complexity, such as oligogenic inheritance, presence of genes acting as risk factors, and reduced penetrance, needs to be considered when interpreting variants. The goal of this work is to provide helpful suggestions to geneticists and clinicians dealing with ALS.
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DeMille, Desiree, Jamie McDonald, Carmelo Bernabeu, Hilary Racher, Carla Olivieri, Claudia Cantarini, Anna Sbalchiero, et al. "Specifications of the ACMG/AMP Variant Curation Guidelines for Hereditary Hemorrhagic Telangiectasia Genes—ENG and ACVRL1." Human Mutation 2024 (May 18, 2024): 1–13. http://dx.doi.org/10.1155/2024/3043736.

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The 2015 ACMG/AMP standards and guidelines for interpretation of sequence variants are widely used by laboratories, including for variant curation of the hereditary hemorrhagic telangiectasia (HHT) genes. However, the need for gene- and disease-specific modifications and specifications of these general guidelines to optimize and standardize variant classification was recognized at the time of publication. With this goal, the ClinGen HHT variant curation expert panel was formed. Here, we describe our recommended HHT-specific variant classification criteria and the outcomes from pilot testing of 30 variants of the ENG and ACVRL1 genes. Eight of the original ACMG/AMP rules were determined to not be applicable for ENG- or ACVRL1-related HHT or were previously recommended by ClinGen for removal, two rules were unmodified, and the remaining 18 rules were modified according to HHT specifications or previous ClinGen general recommendations. This study demonstrates the importance of HHT-specific criteria in the optimization and standardization of HHT variant classification and conflicting classification resolution.
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Dissertations / Theses on the topic "ACMG classification"

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Alameh, Malak. "Phénotypage à haut débit des variants du canal potassique cardiaque hERG." Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1023.

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Le syndrome du QT long de type 2 (SQTL2) héréditaire est un trouble du rythme cardiaque, causé par des mutations perte de fonction dans le gène KCNH2, codant pour le canal ionique cardiaque hERG. Aujourd’hui, plus de 3 000 variants du gène KCNH2 sont répertoriés dans la base de données internationale ClinVar, mais la majorité sont classés comme des variants de signification incertaine (Variant of Unknown Significance en anglais VUS), car leur pathogénicité potentielle n’est pas encore déterminée. La reclassification de ces VUS est primordiale pour améliorer le suivi des personnes affectées. Ce projet a porté sur l'étude de variants identifiés dans la base de données française Bamacœur, avec objectif principal, la reclassification rapide des VUS. Nous avons étudié 303 variants au total reporté dans la base Bamacœur, dont 201 VUS, 81 contrôles pathogènes et 21 contrôles bénins. Tous les paramètres biophysiques sont mesurés, l’amplitude du courant, les courbes d’activation et d’inactivation et les cinétiques. Les valeurs de z-score sont utilisées pour déterminer les zones de fonctionnalité normale ou anormale. La reclassification des VUS est rendue possible grâce aux niveaux de preuve élevés de cette étude. Nous avons identifié 149/201 variants VUS dans la zone de fonction normale et 52 variants dans la zone de fonction anormale. De plus, nous avons identifié des anomalies spécifiques à chaque région de la séquence de hERG. Ces données seront utilisées pour créer une base de données intégrant des informations fonctionnelles, cliniques et génétiques, fournissant un outil précieux pour les cliniciens dans le diagnostic et la prévention du LQTS2
Hereditary long QT syndrome type 2 (LQTS2) is a cardiac rhythm disorder caused by loss-of-function mutations in the KCNH2 gene, encoding the Herg cardiac ion channel. Today, over 3,000 variants of the KCNH2 gene are listed in the international ClinVar database, but the majority are classified as Variants of Unknown Significance (VUS), as their potential pathogenicity has yet to be determined. Reclassification of these VUS is essential to improve follow-up of affected individuals. This project focused on the study of variants identified in the French Bamacœur database, with the main aim of rapidly reclassifying VUSs. We studied 303 variants in total, reported in the French database Bamacoeur including 201 VUS, 81 pathogenic controls and 21 benign controls. All biophysical parameters are measured, including current amplitude, activation and inactivation curves and kinetics. Z-score values are used to determine regions of normal or abnormal functionality. The reclassification of VUS is made possible given the high level of evidence in this study. We identified 151/201 VUS variants in the normal function zone and 50 variants in the abnormal function zone. In addition, we identified regionspecific abnormalities in the hERG sequence. These data will be used to create a database integrating functional, clinical and genetic information, providing a valuable tool for clinicians in the diagnosis and prevention of LQTS2
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Shire, Norah J. "Boosting, Bagging, and Classification Analysis to Improve Noninvasive Liver Fibrosis Prediction in HCV/HIV Coinfected Subjects: An Analysis of the AIDS Clinical Trials Group (ACTG) 5178." Cincinnati, Ohio : University of Cincinnati, 2007. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1172860066.

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Thesis (Ph.D.)--University of Cincinnati, 2007.
Advisor: Charles Ralph Buncher. Title from electronic thesis title page (viewed April 23, 2009). Keywords: Coinfection; Boosting and bagging; Classification analysis; HIV; Viral hepatitis. Includes abstract. Includes bibliographical references.
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Chen, Yinlin. "A High-quality Digital Library Supporting Computing Education: The Ensemble Approach." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78750.

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Educational Digital Libraries (DLs) are complex information systems which are designed to support individuals' information needs and information seeking behavior. To have a broad impact on the communities in education and to serve for a long period, DLs need to structure and organize the resources in a way that facilitates the dissemination and the reuse of resources. Such a digital library should meet defined quality dimensions in the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework - including completeness, consistency, efficiency, extensibility, and reliability - to ensure that a good quality DL is built. In this research, we addressed both external and internal quality aspects of DLs. For internal qualities, we focused on completeness and consistency of the collection, catalog, and repository. We developed an application pipeline to acquire user-generated computing-related resources from YouTube and SlideShare for an educational DL. We applied machine learning techniques to transfer what we learned from the ACM Digital Library dataset. We built classifiers to catalog resources according to the ACM Computing Classification System from the two new domains that were evaluated using Amazon Mechanical Turk. For external qualities, we focused on efficiency, scalability, and reliability in DL services. We proposed cloud-based designs and applications to ensure and improve these qualities in DL services using cloud computing. The experimental results show that our proposed methods are promising for enhancing and enriching an educational digital library. This work received support from ACM, as well as the National Science Foundation under Grant Numbers DUE-0836940, DUE-0937863, and DUE-0840719, and IMLS LG-71-16-0037-16.
Ph. D.
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Herrmann, Kai, Hannes Voigt, and Wolfgang Lehner. "Online horizontal partitioning of heterogeneous data." De Gruyter, 2014. https://tud.qucosa.de/id/qucosa%3A72923.

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In an increasing number of use cases, databases face the challenge of managing heterogeneous data. Heterogeneous data is characterized by a quickly evolving variety of entities without a common set of attributes. These entities do not show enough regularity to be captured in a traditional database schema. A common solution is to centralize the diverse entities in a universal table. Usually, this leads to a very sparse table. Although today’s techniques allow efficient storage of sparse universal tables, query efficiency is still a problem. Queries that address only a subset of attributes have to read the whole universal table includingmany irrelevant entities. Asolution is to use a partitioning of the table, which allows pruning partitions of irrelevant entities before they are touched. Creating and maintaining such a partitioning manually is very laborious or even infeasible, due to the enormous complexity. Thus an autonomous solution is desirable. In this article, we define the Online Partitioning Problem for heterogeneous data. We sketch how an optimal solution for this problem can be determined based on hypergraph partitioning. Although it leads to the optimal partitioning, the hypergraph approach is inappropriate for an implementation in a database system. We present Cinderella, an autonomous online algorithm for horizontal partitioning of heterogeneous entities in universal tables. Cinderella is designed to keep its overhead low by operating online; it incrementally assigns entities to partition while they are touched anyway duringmodifications. This enables a reasonable physical database design at runtime instead of static modeling.
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"An automatic classification of document (ACM) for knowledge management." 2001. http://library.cuhk.edu.hk/record=b6073347.

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Wong Wai-Ting Jacqueline.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2001.
Includes bibliographical references (p. 115-120).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
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Xia, Chaohui. "Web-basierte Methoden zur Untersuchung von Affiliation-Angaben wissenschaftlicher Papiere." 2010. https://ul.qucosa.de/id/qucosa%3A17205.

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Bei der zunehmenden Anzahl von Papers spielt die Affiliationsanalyse eine größer werdende Rolle, man erstrebt zusätzlich zur bibliografischen Analyse, um weitere nützliche Information zu erhalten. So wird z.B. die Affiliation nach Instituten, Countrys, Regionen, Citys und Koordinaten, die aus der Originalaffiliation nicht direkt bekannt sind, analysiert, anschließend kann mit diesen Informationen weiter gearbeitet werden. Man kann die Affiliation mit dem gefundenen Ergebnis kontrollieren und verteilen. Weltweit existieren zahlreiche semantische Analysewerkzeuge. Hier soll die Rede von vier grundsätzlichen Analysemethoden sein und am Ende wird die Auswertung nach einer einzigen Metrik stehen. Durch diesen gefundenen Ergebnisse können wir viele Hinweise gewinnen, z.B. den das Trends der Papers innerhalb von zehn Jahren errechnen und dann weiteres mit Hilfe von GoogleMaps durch die MarkerCluster Technik anzeigen. Auch können wir die Papers nach verschiedenen Ansätzen verlinken. Es wird möglich eine Reihe von Beziehungen definiert, um ein Netz aufzubauen. Dieses kann schnell durchgesehen und es können mächtige Suchstrategien ausgeführt werden. Diese Idee ist unser heutiges Thema und auch unter dem Begriff des Semantik Web bekannt.
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Books on the topic "ACMG classification"

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A FIELD GUIDE TO THE KEY PATTERNS ON THE BACKS OF UNITED STATES CERAMIC TILES, 1870S-1930S (3RD ED.). Architectural Research Associates, Brooklyn, NY, 2010.

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Book chapters on the topic "ACMG classification"

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Kingston, John. "Ontology, Knowledge Management, Knowledge Engineering and the ACM Classification Scheme." In Research and Development in Intelligent Systems XIX, 207–20. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0651-7_15.

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Iqbal, Arif, R. Oppermann, A. Patel, and Kinshuk. "A Classification of Evaluation Methods for Intelligent Tutoring Systems." In Berichte des German Chapter of the ACM, 169–81. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-99786-9_16.

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Zhuk, Dmitriy, Barnaby Martin, and Michał Wrona. "The complete classification for quantified equality constraints." In Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2746–60. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977554.ch103.

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Cai, Jin-Yi, Zhiguo Fu, and Shuai Shao. "New Planar P-time Computable Six-Vertex Models and a Complete Complexity Classification." In Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA), 1535–47. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976465.93.

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Guardo, Elena, and Adam Van Tuyl. "Classification of ACM sets of points in ℙ 1 × ℙ 1 $$\mathbb{P}^{1} \times \mathbb{P}^{1}$$." In Arithmetically Cohen-Macaulay Sets of Points in P^1 x P^1, 41–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24166-1_4.

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Henson, John W., and Robert G. Resta. "Table 10. ACMG rules for variant classification." In Diagnosis and Management of Hereditary Cancer, 71–72. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-323-90029-4.00010-9.

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Cillessen, Felix, Pim Steenbergh, and Jacob Hofdijk. "Towards Regional Population Health Management: A Prospective Analysis Using the Adjusted Clinical Groups Classification." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240430.

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This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients’ burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.
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M. Harvey, Evan, Murad Almasri, and Hugo R. Martinez. "Genetics of Cardiomyopathy." In Cardiomyopathy - Disease of the Heart Muscle [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97010.

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Cardiomyopathies (CMs) encompass a heterogeneous group of structural and functional (systolic and diastolic) abnormalities of the myocardium and are either confined to the cardiovascular system or are part of a systemic disorder. CMs represent a leading cause of morbidity and mortality and account for a significant percentage of death and cardiac transplantation. The 2006 American Heart Association (AHA) classification grouped CMs into primary (genetic, mixed, or acquired) or secondary (i.e., infiltrative or autoimmune). In 2008, the European Society of Cardiology classification proposed subgrouping CM into familial or genetic and nonfamilial or nongenetic forms. In 2013, the World Heart Federation recommended the MOGES nosology system, which incorporates a morpho-functional phenotype (M), organ(s) involved (O), the genetic inheritance pattern (G), an etiological annotation (E) including genetic defects or underlying disease/substrates, and the functional status (S) of a particular patient based on heart failure symptoms. Rapid advancements in the biology of cardio-genetics have revealed substantial genetic and phenotypic heterogeneity in myocardial disease. Given the variety of disciplines in the scientific and clinical fields, any desired classification may face challenges to obtaining consensus. Nonetheless, the heritable phenotype-based CM classification offers the possibility of a simple, clinically useful diagnostic scheme. In this chapter, we will describe the genetic basis of dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), arrhythmogenic cardiomyopathy (ACM), LV noncompaction cardiomyopathy (LVNC), and restrictive cardiomyopathy (RCM). Although the descriptive morphologies of these types of CM differ, an overlapping phenotype is frequently encountered within the CM types and arrhythmogenic pathology in clinical practice. CMs appear to originate secondary to disruption of “final common pathways.” These disruptions may have purely genetic causes. For example, single gene mutations result in dysfunctional protein synthesis causing downstream dysfunctional protein interactions at the level of the sarcomere and a CM phenotype. The sarcomere is a complex with multiple protein interactions, including thick myofilament proteins, thin myofilament proteins, and myosin-binding proteins. In addition, other proteins are involved in the surrounding architecture of the sarcomere such as the Z-disk and muscle LIM proteins. One or multiple genes can exhibit tissue-specific function, development, and physiologically regulated patterns of expression for each protein. Alternatively, multiple mutations in the same gene (compound heterozygosity) or in different genes (digenic heterozygosity) may lead to a phenotype that may be classic, more severe, or even overlapping with other disease forms.
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Conference papers on the topic "ACMG classification"

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Alesci, S. R., C. Hecking, B. Rackè, D. Janssen, and E. C. Dempfle. "Utility of ACMG classification to support interpretation of molecular genetic test results in patients with FVII deficiency." In GTH Congress 2023 – 67th Annual Meeting of the Society of Thrombosis and Haemostasis Research – The patient as a benchmark. Georg Thieme Verlag, 2023. http://dx.doi.org/10.1055/s-0042-1760573.

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Wang, Tao, and Bipin C. Desai. "Document Classification with ACM Subject Hierarchy." In 2007 Canadian Conference on Electrical and Computer Engineering. IEEE, 2007. http://dx.doi.org/10.1109/ccece.2007.203.

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De Chiara, Rosario, and Andrew Fish. "EulerView: article organisation within the ACM classification." In 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 2008. http://dx.doi.org/10.1109/vlhcc.2008.4639066.

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Lin, Xia, Mi Zhang, Haozhen Zhao, and Jan Buzydlowski. "Multi-view of the ACM classification system." In the 12th ACM/IEEE-CS joint conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2232817.2232909.

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Senel, Numan, Shrivatsa Udupa, and Gordon Elger. "Sensor Data Preprocessing in Road-Side Sensor Units." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-acm-120.

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To improve safety, mitigate traffic congestion and to reduce pollution caused by vehicles, infrastructure-side sensors can be used, especially at critical locations in cities. In the future, infrastructural safeguarding has large potential, due to availability of advanced sensors (camera, radar, lidar) and vehicle-to-infrastructure(V2I) communications. Currently, camera-based systems are widely used to monitor traffic violations. A smart combination of multiple sensors like camera-RADAR or camera-LIDAR is used to determine the precise velocity and position of the traffic participants. In such scenarios RADAR/LIDAR will be responsible for detection of velocity or position and cameras will be used to identify the traffic participants, i.e. for object classification. However, processing of large amount of data is necessary at the sensor nodes. With the evolution of technology and availability of higher computational power, such systems will become affordable and smarter. Additional hardware can enable such systems to communicate with other traffic participants in order to increase safety and efficiency. Additional hardware and computational power will be limited due to cost overhead, size, weather conditions and power consumption limitations in the open-air roads. To mitigate such limitations, we have could-based solutions where data are acquired at the road side units but processed remotely in the cloud. Although it is a valid solution, it brings limitation regarding the required high bandwidth and is also a potential threat for data leaks, e.g. privacy and data security. To have a large detection range a camera imager needs to have a large chip area and high number of pixels. Therefore, the image size gets large even if the large number of pixels is not required for objects in short distance. In this paper an image pre-processing method is developed to reduce the sensor data size, which in turn reduces the computational power to process or the bandwidth to transmit the data. An increase of detection range is possible keeping the data size at an acceptable level. Reducing the sensor data size is a benefit and reduces the dependency of cloud-based solutions. Even in case of using a cloud-based solution, reduced data size will result in a lower network load, that increase overall performance of could base systems. In the paper, YOLO-V3 is used for object detection and classification of traffic participants. In Addition, the fixed installation of the camera in the infrastructure allows to apply methods for depth estimation when using only mono cameras. The improvement and accuracy of the depth estimation is benchmarked using data from RADAR and LiDAR sensors as ground truth, which are installed at the same sensor node as the camera, i.e. the data of Radar and LiDAR are fused to the camera data.
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Lopez,, Guillaume, Ko Uchida, and Atsunobu Kaminuma. "Guide Timing Control for In-vehicle Speech Interface using Driving Signal." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-acm-084.

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In recent years, speech interfaces have been proposed for making shopping order or restaurant reservations while transmitting information only by speech. It is easy to imagine that such a system will be installed in cars soon. However, to use the speech interface that can realize such tasks while the driver is driving, the system needs to understand the driving situation of the driver and control the speech guide timing accordingly. We focused on the fact that there are scenes (SCD: Scenes of Concentrate Driving) where the driver temporarily interrupts the speech operation unconsciously to concentrate on driving operations. However, one does not yet know in which scenes the voice dialogue becomes a burden. In this study, we identified driving scenes where dialogue is a burden (SCD), clarified the relationship between SCD and driving behavior, and considered a method to estimate SCD automatically. In this research, we define SCD as a situation in which the driver temporarily suspends the speech operation and wants to perform only the driving operation, although the system can perform both the speech operation and the driving operation. Under SCD circumstances, even if the speech interface presents some guidance, the driver can not understand its contents. Also, even if the system prompts for an answer by speech input, the driver does not answer. To confirm the existence of SCD, we have created nine driving scenes that require different driving operations (lane change, overtaking, narrow road, right turn, etc.) and reproduced these scenes using a Driving Simulator (DS). We also created some tasks to prompt the driver's voice input, and present them just before the driving scene, such we could check whether the driver interrupts or not the dialogue during the driving operations needed to pass the scene. We recorded speech and collected driving signals (steering angle, throttle opening, etc.) for 15 men and women in their 20s (11 men and 4 women) with a driver's license. First, for each driving scene, we checked whether the driver could answer the immediately preceding question presented by an operator and confirms that SCD occurs in various driving scenes. In the driving scenes involving lane change, the utterance was interrupted for more than 40% of the tasks. On the other hand, in the driving scenes in which the driver steers left and right curves, while driving in the same lane as the preceding vehicle, the utterance was seldom interrupted. These results confirm the existence of driving scenes in which speech is likely to be interrupted, that is, SCD. Next, we considered a machine learning model to estimate the occurrence of SCD automatically, using only the driving signal. We compared the classification accuracy of SCD occurrence according to several analysis window lengths, from 0.5seconds to 5 seconds with a 0.5seconds increment. The results showed that a trained Support Vector Machine (SVM) could provide a classification accuracy of about to 85% for two classes (SCD or not), and 82% for three classes (SCD, semi-SCD, regular) with a window size of 2seconds.
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Tome, Paulo. "Implementation of Data Science Techniques in the ACM Computing Classification System." In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2022. http://dx.doi.org/10.1109/iceccme55909.2022.9988283.

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Guturobu, Gabriel, Mihai Dascalu, Dominic Heutelbeck, Matthias Hemmje, Wim Westera, and Stefan Trausanmatu. "SEMANTIC ANNOTATION AND AUTOMATED TEXT CATEGORIZATION USING COHESION NETWORK ANALYSIS." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-177.

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With the increasing amount of published scientific papers, it becomes paramount for learners and researchers alike to use tools that semantically annotate resources in order to facilitate the information retrieval process. Thus, we introduce a semantic annotation tool incorporated within our ReaderBench framework to provide recommendations regarding categories that should be used for automated labelling. Currently, the tool categorizes input documents based on the ACM Computing Classification System (http://dl.acm.org/ ccs_flat.cfm) taxonomy from 2012. The Semantic Annotation tool provides also suggestions and cohesion scores for the most relevant keywords covered by the paper, allowing researchers to automatically extract the paper’s topics. Therefore, the semantic annotation algorithms involved within the tool ensure a cohesion-centered and in-depth representation of discourse. The underlying adaptive technologies support academia with potential suggestions of automated categorization and keywords generation useful when submitting scientific papers or properly assigning papers for review. A more specific objective is to facilitate the classification of publications in the internal Digital Library of the RAGE project meant to support researchers from the RAGE eco-system. Additionally, the Semantic Annotation tool provides cohesion scores between the abstract, authors’ keywords and the entire paper’s textual content. These scores may provide useful insights in terms of generating personalized recommendations of keywords that are representative for an article, or recommendations for rewriting the abstract in a cohesive manner in accordance with the entire paper. To this aim, the SemEval corpora comprising of 244 scientific papers classified into four of the ACM CCS 1998 categories was used to validate our tool. Hence, we applied a clustering algorithm to group semantically related papers and compared the generated clusters with the initial group assignments.
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Wang, Leiqi, Zijun Cheng, Qiujian Lv, Yan Wang, Shixiang Zhang, and Weiqing Huang. "ACG: Attack Classification on Encrypted Network Traffic using Graph Convolution Attention Networks." In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152599.

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Buzydlowski, Jan W., and Lillian N. Cassel. "Visualizing the Evolution of Information Retrieval via the ACM Computer Classification Codes." In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE, 2021. http://dx.doi.org/10.1109/jcdl52503.2021.00059.

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