To see the other types of publications on this topic, follow the link: Algorithmes de fusion.
Journal articles on the topic 'Algorithmes de fusion'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Algorithmes de fusion.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Thomson, Ashlee J., Jacqueline A. Rehn, Susan L. Heatley, Laura N. Eadie, Elyse C. Page, Caitlin Schutz, Barbara J. McClure, et al. "Reproducible Bioinformatics Analysis Workflows for Detecting IGH Gene Fusions in B-Cell Acute Lymphoblastic Leukaemia Patients." Cancers 15, no. 19 (September 26, 2023): 4731. http://dx.doi.org/10.3390/cancers15194731.
Full textAbstract:
B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by diverse genomic alterations, the most frequent being gene fusions detected via transcriptomic analysis (mRNA-seq). Due to its hypervariable nature, gene fusions involving the Immunoglobulin Heavy Chain (IGH) locus can be difficult to detect with standard gene fusion calling algorithms and significant computational resources and analysis times are required. We aimed to optimize a gene fusion calling workflow to achieve best-case sensitivity for IGH gene fusion detection. Using Nextflow, we developed a simplified workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion. We analysed samples from 35 patients harbouring IGH fusions (IGH::CRLF2 n = 17, IGH::DUX4 n = 15, IGH::EPOR n = 3) and assessed the detection rates for each caller, before optimizing the parameters to enhance sensitivity for IGH fusions. Initial results showed that FusionCatcher and Arriba outperformed STAR-Fusion (85–89% vs. 29% of IGH fusions reported). We found that extensive filtering in STAR-Fusion hindered IGH reporting. By adjusting specific filtering steps (e.g., read support, fusion fragments per million total reads), we achieved a 94% reporting rate for IGH fusions with STAR-Fusion. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL subtypes.
2
Carrara, Matteo, Marco Beccuti, Fulvio Lazzarato, Federica Cavallo, Francesca Cordero, Susanna Donatelli, and Raffaele A. Calogero. "State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/340620.
Full textAbstract:
Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.
3
Fu Hongyu, 付宏语, 巩岩 Gong Yan, 汪路涵 Wang Luhan, 张艳微 Zhang Yanwei, 郎松 Lang Song, 张志 Zhang Zhi, and 郑汉青 Zheng Hanqing. "多聚焦显微图像融合算法." Laser & Optoelectronics Progress 61, no. 6 (2024): 0618022. http://dx.doi.org/10.3788/lop232015.
Full text
4
Tan, Yuxiang, Yann Tambouret, and Stefano Monti. "SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/780519.
Full textAbstract:
The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.
5
Dehghannasiri, Roozbeh, Donald E. Freeman, Milos Jordanski, Gillian L. Hsieh, Ana Damljanovic, Erik Lehnert, and Julia Salzman. "Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers." Proceedings of the National Academy of Sciences 116, no. 31 (July 15, 2019): 15524–33. http://dx.doi.org/10.1073/pnas.1900391116.
Full textAbstract:
The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling 10-fold fewer false-positive fusions in nontransformed human tissues. We leverage the increased precision of DEEPEST to discover fundamental cancer biology. Namely, 888 candidate oncogenes are identified based on overrepresentation in DEEPEST calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs, demonstrating a previously unappreciated prevalence and potential for function. DEEPEST also reveals a high enrichment for fusions involving oncogenes in cancers, including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. Specific protein domains are enriched in DEEPEST calls, indicating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.
6
Nandeesh, M. D., and Dr M. Meenakshi. "Image Fusion Algorithms for Medical Images-A Comparison." Bonfring International Journal of Advances in Image Processing 5, no. 3 (July 31, 2015): 23–26. http://dx.doi.org/10.9756/bijaip.8051.
Full text
7
Karan, Canan, Elaine Tan, Humaira Sarfraz, Christine Marie Walko, Richard D. Kim, Todd C. Knepper, and Ibrahim Halil Sahin. "Clinical and molecular characterization of fusion genes in colorectal cancer." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e15568-e15568. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e15568.
Full textAbstract:
e15568 Background: Next-generation sequencing (NGS) based molecular profiling technologies have revealed several oncogenic fusion genes that are actionable with small molecule inhibitors leading to practice change, particularly in lung cancer. The molecular and clinical characteristics of these gene fusions are not well defined in colorectal cancer patients (CRC). In this study, we aimed to define clinical and molecular characteristics of fusion genes in patients with CRC who underwent molecular profiling. Methods: Molecular characteristics of tissue confirmed 917 CRC patients were retrieved from the Moffit Cancer Center Clinical Genomics Action Committee database. Patients’ demographic and clinicopathological features and treatment history were collected from the database. All fusion genes were shown by hybridization-based NGS computational algorithms that determined cancer‐related genes, including single‐nucleotide variations, indels, microsatellite instability (MSI) status. Results: Among a total of 917 patients, 24 patients with CRC (2.6%) were found to have at least one fusion gene with a total number of 26 pathogenic fusions. The gene fusions are shown in Table. The most common, potentially targetable, fusion genes in our cohort were (1) RET fusions 0.5% (5/917), (2) ALK fusions 0.4% (4/917), (3) ROS1 fusions 0.2% (2/917), (4) NTRK1 fusion 0.1% (1/917), (5) NRG1 fusion 0.1% (1/917). Fusion genes were more common in MSI-H CRC (N = 27), and 3 (11.1%) patients with MSI-H CRC were found to have fusion genes [(RET (2) and NTRK(1)]. Fusion genes were present in both RAS wild-type (54%; 13/24) and RAS mutant (46%; 11/24) tumors. Most patients were older than 50 years (75%, 18/24) and had left-sided tumor (61.1%) tumor. Conclusions: Fusion genes are rare events in CRC. While fusion genes seem to be more prevalent in MSI-H CRC, RAS status does not correlate with the frequency of fusion genes. Actionable RET and ALK/ROS gene fusion are more common than NTRK fusion genes in this cohort of CRC patients.[Table: see text]
8
Foltz, Steven M., Qingsong Gao, Christopher J. Yoon, Amila Weerasinghe, Hua Sun, Lijun Yao, Mark A. Fiala, et al. "Comprehensive Multi-Omics Analysis of Gene Fusions in a Large Multiple Myeloma Cohort." Blood 132, Supplement 1 (November 29, 2018): 1898. http://dx.doi.org/10.1182/blood-2018-99-117245.
Full textAbstract:
Abstract Introduction: Gene fusions are the result of genomic rearrangements that create hybrid protein products or bring the regulatory elements of one gene into close proximity of another. Fusions often dysregulate gene function or expression through oncogene overexpression or tumor suppressor underexpression (Gao, Liang, Foltz, et al. Cell Rep 2018). Some fusions such as EML4--ALK in lung adenocarcinoma are known druggable targets. Fusion detection algorithms utilize discordantly mapped RNA-seq reads. Careful consideration of detection and filtering procedures is vital for large-scale fusion detection because current methods are prone to reporting false positives and show poor concordance. Multiple myeloma (MM) is a blood cancer in which rapidly expanding clones of plasma cells spread in the bone marrow. Translocations that juxtapose the highly-expressed IGH enhancer with potential oncogenes are associated with overexpression of partner genes, although they may not lead to a detectable gene fusion in RNA-seq data. Previous studies have explored the fusion landscape of multiple myeloma cohorts (Cleynen, et al. Nat Comm 2017; Nasser, et al. Blood 2017). In this study, we developed a novel gene fusion detection pipeline and post-processing strategy to analyze 742 patient samples at the primary time point and 64 samples at follow-up time points (806 total samples) from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study using RNA-seq, WGS, and clinical data. Methods and Results: We overlapped five fusion detection algorithms (EricScript, FusionCatcher, INTEGRATE, PRADA, and STAR-Fusion) to report fusion events. Our filtered call set consisted of 2,817 fusions with a median of 3 fusions per sample (mean 3.8), similar to glioblastoma, breast, ovarian, and prostate cancers in TCGA. Major recurrent fusions involving immunoglobulin genes included IGH--WHSC1 (88 primary samples), IGL--BMI1 (29), and the upstream neighbor of MYC, PVT1, paired with IGH (6), IGK (3), and IGL (11). For each event, we used WGS data when available to determine if there was genomic support of the gene fusion (based on discordant WGS reads, SV event detection, and MMRF CoMMpass Seq-FISH WGS results) (Miller, et al. Blood 2016). WGS validation rates varied by the level of RNA-seq evidence supporting each fusion, with an overall rate of 24.1%, which is comparable to previously observed pan-cancer validation rates using low-pass WGS. We calculated the association between fusion status and gene expression and identified genes such as BCL2L11, CCND1/2, LTBR, and TXNDC5 that showed significant overexpression (t-test). We explored the clinical connections of fusion events through survival analysis and clinical data correlations, and by mining potentially druggable targets from our Database of Evidence for Precision Oncology (dinglab.wustl.edu/depo) (Sun, Mashl, Sengupta, et al. Bioinformatics 2018). Major examples of upregulated fusion kinases that could potentially be targeted with off-label drug use include FGFR3 and NTRK1. We examined the evolution of fusion events over multiple time points. In one MMRF patient with a t(8;14) translocation joining the IGH locus and transcription factor MAFA, we observed IGH fusions with TOP1MT (neighbor of MAFA) at all four time points with corresponding high expression of TOP1MT and MAFA. Using non-MMRF single-cell RNA data from different patients, we were able to track cell-type composition over time as well as detect subpopulations of cells harboring fusions at different time points with potential treatment implications. Discussion: Gene fusions offer potential targets for alternative MM therapies. Careful implementation of gene fusion detection algorithms and post-processing are essential in large cohort studies to reduce false positives and enrich results for clinically relevant information. Clinical fusion detection from untargeted RNA-seq remains a challenge due to poor sensitivity, specificity, and usability. By combining MMRF CoMMpass data from multiple platforms, we have produced a comprehensive fusion profile of 742 MM patients. We have shown novel gene fusion associations with gene expression and clinical data, and we identified candidates for druggability studies. Disclosures Vij: Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.
9
Thomas, Brad B., Yanglong Mou, Lauryn Keeler, Christophe Magnan, Vincent Funari, Lawrence Weiss, Shari Brown, and Sally Agersborg. "A Highly Sensitive and Specific Gene Fusion Algorithm Based on Multiple Fusion Callers and an Ensemble Machine Learning Approach." Blood 136, Supplement 1 (November 5, 2020): 12–13. http://dx.doi.org/10.1182/blood-2020-142020.
Full textAbstract:
Background: Gene Fusion events are common occurrences in malignancies, and are frequently drivers of malignancy. FISH and qPCR are two methods often used for identifying highly prevalent gene fusions/translocations. However, these are single target assays, requiring a lot of effort and sample if multiple assays are needed for multiple targets like sarcoma. High-throughput parallel (NextGen) DNA and RNA sequencing are also in current use to detect and characterize gene fusions. RNA sequencing (RNAseq) has the advantage that multiple markers can be targeted at one time and RNA fusions are readily identified from their product transcripts. While many fusion calling algorithms exist for use on RNAseq data, sensitive fusion callers, needed for samples of low tumor content, often present high false positive rates. Further, there currently is no single variable or element in NGS data that can be used to filter out false positive calls by extant callers. Individual sensitive fusion callers may be considered weak predictors of gene fusions. Combining their results into a single fusion call involves evaluating many elements, which can be a time consuming and difficult manual task. In order to achieve higher accuracy in fusion calls than can be achieved using individual fusion callers, we have combined the results of multiple fusion callers by use of an ensemble learning approach based on random forest models. Our method selects the best group of callers from among several callers, and provides an algorithmic means of combining their results, presenting a metric that can be immediately interpreted as the probability that a called fusion is a true fusion call. Methods: Random forest models were generated with the randomForest package in R, and then tuned using the R caret package. Training data sets consisted of fusion calls deemed true by review and by orthogonal methods including PCR/Sanger sequencing and the commercial Archer™ fusion calling system. We present the results of training on calls made by five fusion callers Arriba, STAR-Fusion, FusionCatcher, deFuse, and Kallisto/pizzly. Logistic training variables (seen vs not seen by the fusion caller) were used for the five callers. Variables also included metrics for the magnitude and balance of coverage on either side of candidate fusion breakpoints reported by Arriba and STAR Fusion ("coverage balance") and a single metric consisting of the number of sequencing reads that cross the candidate breakpoint. The model was validated by 10-fold cross-validation on 598 fusion calls by the five callers. Results: The resulting model is superior to the simple strategy of requiring agreement by n of five callers, particularly with regard to specificity (Table 1). Also, "importance of variables," reported by randomForest, gauges the relative contribution of variables in the model. Here it shows that one caller, Kallisto\pizzly, does not contribute to the model (Table 2). Conclusion: Random Forest modeling provides a viable means of combining gene fusion call data from multiple callers into a single fusion calling tool with improved performance over simple combinations of fusion calls. An additional benefit is seen in that building and evaluating such models can guide the selection of fusion callers, thereby eliminating non-contributory calling methods and ensuring optimal utilization of computational resources. Disclosures Thomas: NeoGenomics,Inc.: Current Employment. Mou:NeoGenomics: Current Employment. Keeler:NeoGenomics: Current Employment. Magnan:NeoGenomics: Current Employment. Funari:NeoGenomics: Current Employment. Weiss:Merck: Other: Speaker; Bayer: Other: speaker; Genentech: Other: Speaker; NeoGenomics: Current Employment. Brown:NeoGenomics,Inc.: Current Employment. Agersborg:NeoGenomics: Current Employment.
10
Sun, Changqi, Cong Zhang, and Naixue Xiong. "Infrared and Visible Image Fusion Techniques Based on Deep Learning: A Review." Electronics 9, no. 12 (December 17, 2020): 2162. http://dx.doi.org/10.3390/electronics9122162.
Full textAbstract:
Infrared and visible image fusion technologies make full use of different image features obtained by different sensors, retain complementary information of the source images during the fusion process, and use redundant information to improve the credibility of the fusion image. In recent years, many researchers have used deep learning methods (DL) to explore the field of image fusion and found that applying DL has improved the time-consuming efficiency of the model and the fusion effect. However, DL includes many branches, and there is currently no detailed investigation of deep learning methods in image fusion. In this work, this survey reports on the development of image fusion algorithms based on deep learning in recent years. Specifically, this paper first conducts a detailed investigation on the fusion method of infrared and visible images based on deep learning, compares the existing fusion algorithms qualitatively and quantitatively with the existing fusion quality indicators, and discusses various fusions. The main contribution, advantages, and disadvantages of the algorithm. Finally, the research status of infrared and visible image fusion is summarized, and future work has prospected. This research can help us realize many image fusion methods in recent years and lay the foundation for future research work.
11
Cleynen, Alice, Raphael Szalat, Mehmet Kemal Samur, Naim Rashid, Giovanni Parmigiani, Nikhil C. Munshi, and Hervé Avet-Loiseau. "Frequent Igh Fusion Transcripts with Clinical Impact in Multiple Myeloma." Blood 124, no. 21 (December 6, 2014): 721. http://dx.doi.org/10.1182/blood.v124.21.721.721.
Full textAbstract:
Abstract Background: Significant heterogeneity has been described in Multiple Myloma (MM), especially at the genomic level. Frequent gains and losses of DNA along with various mutations have been observed, and differential allelic expression is being characterized. Fusion proteins are common and maybe associated with cell transformation, growth and lethality of tumor cells. However, unlike in leukemia, no consistent fusion gene product has been consistently identified. As IgH-related translocations have an important role in myeloma, we have investigated fusion genes involving IgH, to understand their biology and explore a possible effect on survival in MM. Methods: We performed deep RNA-Seq on purified MM cells from 430 newly-diagnosed MM patients and analyzed gene expression profiles, isoform signatures and both novel and known fusion genes using two common algorithms: TopHat and MapSplice. We also correlated genomic data with patient data including cytogenetics and FISH, as well as survival. Results: We primarily focused on fusions involving the IGH gene (chromosome 14) and found that about one fourth of the patients (57 out of 430) presented an IGH fusion gene (97 patients according to TopHat, 303 according to Mapsplice, 57 according to both). These included the well described t(4,14) fusion involving the MMSET gene (found in 47 patients by both algorithms, 49 by Tophat, 54 by Mapsplice). Additionally we observed fusions involving chromosomes 14 and chromosomes 1, 4, 11, 12, and 16. The counterpart genes involved in the IgH fusions included PDE3A (chromosome 12 - 4 patients); HFM1 (chromosome 1, 2 patients); NFKB1, FGFR3, CIITA, WWOX and MRPL21 (chromosomes 4, 16, and 11, 1 patient). As RNA-Seq data allows the precise localization of the breakpoints, we were able to identify that out of the 47 t(4,14) patients, 62% were MB4-1, 9.5% were MB4-2 and 28.5% were MB4-3. Interestingly, we did not see fusion products involving IgH and other known parts on Chromosomes 8 and 20. We studied event-free survival in a subset of 265 patients with available survival data and found that, as predicted, patients with an IgH-MMSET fusion had significantly lower survival than others. However, patients with a fusion gene involving IGH and any other partner have a significantly better prognosis as a group. Moreover, the poor prognosis of IgH-MMSET fusion appears to be driven by MB4-3 patients. Importantly, the fusions identified using RNA-seq were also validated by FISH analysis. All t(4,14) fusions were characterized by a very high MMSET expression (FPKM greater than 20) while patients with other fusions presented a lower MMSET expression (FPKM lower than 10). Conclusion: Our study suggests that IgH-related translocations in myeloma may impact tumor biology by a number of mechanism, one of which is the generation of fusion proteins with functional consequences. It also highlights a possible clinical impact that requires validation in larger cohorts. Disclosures No relevant conflicts of interest to declare.
12
Liang Liming, 梁礼明, 尹江 Yin Jiang, 吴媛媛 Wu Yuanyuan, and 冯骏 Feng Jun. "基于双边融合的医学图像分割算法." Laser & Optoelectronics Progress 59, no. 8 (2022): 0817003. http://dx.doi.org/10.3788/lop202259.0817003.
Full text
13
Liu Tong, 刘通, 高思洁 Gao Sijie, and 聂为之 Nie Weizhi. "基于多模态信息融合的多目标检测算法." Laser & Optoelectronics Progress 59, no. 8 (2022): 0815002. http://dx.doi.org/10.3788/lop202259.0815002.
Full text
14
Wei Chunmiao, 韦春苗, 徐岩 Xu Yan, and 李媛 Li Yuan. "基于小波变换的迭代融合去雾算法." Laser & Optoelectronics Progress 58, no. 20 (2021): 2010018. http://dx.doi.org/10.3788/lop202158.2010018.
Full text
15
Tan Wei, 谭威, 宋闯 Song Chuang, 赵佳佳 Zhao Jiajia, and 梁欣凯 Liang Xinkai. "基于多层级图像分解的图像融合算法." Infrared and Laser Engineering 51, no. 8 (2022): 20210681. http://dx.doi.org/10.3788/irla20210681.
Full text
16
Priedigkeit, Nolan, Alinés Lebrón-Torres, Janny Liao, Jean-Baptiste Alberge, Stefania Morganti, Jakob Weiss, Jorge Gomez Tejeda Zanudo, et al. "Abstract GS03-09: Characterization and proposed therapeutic exploitation of fusion RNAs in metastatic breast cancers." Cancer Research 84, no. 9_Supplement (May 2, 2024): GS03–09—GS03–09. http://dx.doi.org/10.1158/1538-7445.sabcs23-gs03-09.
Full textAbstract:
Abstract BACKGROUND: Large-scale genomic studies such as The Cancer Genome Atlas (TCGA) and Pan-Cancer Analysis of Whole Genomes (PCAWG) show that the breast cancer (BrCa) genome is dominated by structural variation (SV) rather than single base pair mutations, producing a fertile environment for gene fusions. In this study, we implement a rigorous, expression-based approach to create a comprehensive landscape of fusion RNAs in metastatic breast cancer (MBC). We find fusion RNAs—many of which are novel involving known oncogenes—are surprisingly common in the advanced setting and credential their use as base-editing therapeutic targets. METHODS: Two retrospective cohorts of MBC RNA-sequencing data were analyzed— Dana-Farber Cancer Institute CCPM (n = 252 cases, 276 specimens), MichiganCSER (n = 171 cases, 190 specimens)—with a Fusion MetaCaller that integrates 5 unsupervised fusion-finding algorithms. Fusion RNAs identified in at least 2 callers (High-Confidence) and absent in RNA-seq from normal tissue (Cancer-Specific) were classified as HCCS-Fusions. Further removal of common artifactual fusions was performed using public databases. Expression of each HCCS-Fusion was quantified using a supervised method (FusionInspector)—expressed HCCS-Fusions were defined as having a Fusion Fragment Per Million (FFPM) value > 0.1 and at least 10% of read counts mapping to the fusion breakpoint versus the flanking 5’ or 3’ partners’ exons. Normalized gene-level expression abundances were calculated to correlate transcriptomic features (gene expression, PAM50) with fusion RNAs. Recurrent, potentially pathogenic fusion RNAs were annotated using OncoKB and outlier expressed fusions (Q3 FFPM + [1.5 X IQR]) were interrogated. RESULTS: The frequency of HCCS-Fusions differed between subtypes with basal BrCa harboring the most per tumor followed by Her2, LumB and LumA—with a median HCCS-Fusion count of 13, 12, 7, 4 respectively. 64.5% of cases harbored a HCCS-Fusion in an OncoKB cancer-related gene. The most recurrent fusions involving a cancer-related gene were 5’ ESR1 fusions (14 cases)—all with in-frame breakpoints near exon6/7, disrupting ESR1’s ligand binding domain. 13 of 14 ESR1 fusions were called in Luminal B (LumB) metastases (Fisher’s exact enrichment p < 0.005 vs other subtypes)—defining an ESR1 fusion frequency of 6.5% in LumB disease. Beyond ESR1, we identify recurrent, low-frequency (2-4 cases) in-frame kinase fusions involving FGFR2, ADK, TLK2, PRKCA, BRAF, CHKA, CSNK1D, NEK11, TNIK—some potentially targetable with FDA-approved small molecule inhibitors—as well as recurrent, predicted loss-of-function fusion RNAs in NF1, MSI2, USP32, PTEN, and CDH1. Lastly, 33.6% of cases harbored at least one outlier expressed fusion RNA; including highly expressed in-frame fusions involving known BrCa mediators such as ERBB2, BRCA1, ARID1B, RPS6KB1/2, PIK3R3, AXIN1, TGFB1/2, FOXP1, PAK1, and CREBBP. CONCLUSIONS: Taken together, these results demonstrate that fusion RNAs in MBC—some recurrent, many highly expressed and unique to individual tumors—are common. We create the most comprehensive catalog of ESR1 fusions in MBC, better define their frequency, discover their enrichment in LumB-like tumors, and will discuss clinicopathologic and transcriptomic features associated with ESR1 fusion positive disease. We identify druggable fusions that would likely be missed by current testing standards, find recurrent loss-of-function fusion RNAs, and show that over one-third of metastatic cases harbor at least one outlier expressed fusion—many of which involve BrCa-related genes. In summary, we propose that fusion RNAs are a driving and perhaps overlooked mechanism of tumor evolution in therapy-resistant disease and postulate fusion transcripts present a compelling therapeutic opportunity in MBC. Preliminary data targeting fusion RNA breakpoints using a novel RNA base-editing approach will be discussed. Citation Format: Nolan Priedigkeit, Alinés Lebrón-Torres, Janny Liao, Jean-Baptiste Alberge, Stefania Morganti, Jakob Weiss, Jorge Gomez Tejeda Zanudo, Albert Grinshpun, Melissa Hughes, Karla Helvie, Kerry Sendrick, Kyleen Nguyen, Sarah Strauss, Janet Files, Maxwell Lloyd, Nikhil Wagle, Chip Stewart, Eric Winer, Bruce Johnson, Yvonne Li, Rinath Jeselsohn, Sara Tolaney, Daniel Abravanel, Nancy Lin, Heather Parsons, Gad Getz, Steffi Oesterreich, Adrian Lee, Todd Golub. Characterization and proposed therapeutic exploitation of fusion RNAs in metastatic breast cancers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS03-09.
17
Cleynen, Alice, Raphaël Szalat, Mehmet Kemal Samur, Giovanni Parmigiani, Nikhil C. Munshi, and Hervé Avet-Loiseau. "The Fusion Gene Landscape in Multiple Myeloma, with Clinical Impact." Blood 126, no. 23 (December 3, 2015): 835. http://dx.doi.org/10.1182/blood.v126.23.835.835.
Full textAbstract:
Abstract Background: Gene fusions play an important role in aberrant cellular biology as well as development and progression of cancer. Expression of fusion genes such as PML-RAR drives the transformation in APL and provides important targets for therapy. However, in multiple Myeloma (MM), a heterogeneous disease characterized by genomic instability, frequent gains and losses of DNA, and a diverse mutational landscape, only the well characterized MMSET-IGH fusion product has been reported. Here we investigate the fusion gene landscape in multiple myeloma, and its possible impact on survival. Method: Deep RNA-Seq was performed on purified MM cells from 430 newly-diagnosed MM patients, 20 normal individuals and 71 cell-lines; data were analyzed for gene expression profiles, long-non coding RNA signatures, and both novel and known fusion genes using two common algorithms: TopHat and MapSplice. MM characteristics, cytogenetic and FISH as well as clinical survival outcomes were also analyzed and correlated with genomic data. Results: After filtering candidate fusions linking genes belonging to the same family, we identified 416 different candidates in myeloma patients, 40 % of which identified either IGH or Kappa as a partner. IGH fusion partners included the previously described and validated WHSC1 and B2M genes, as well as over 50 new candidates, while more than 70 different partners were found to be fused with Kappa. These genes exhibit functional enrichment of positive regulators of the cytokine-mediated signaling pathway, negative regulation of myeloid cell differentiation, negative regulation of interleukin-6 production, as well as others. 31% of patients presented no fusions, and another 32% presented a single fusion event. The other 37% presented at least 2 fusion candidates, with up to 27 different candidates. Similar patterns were observed in cell-lines, with 196 unique candidates identified, only 16% of which involving IGH or kappa. However, all partners were found in at least one patient as well. Only 12% of cell-lines exhibited no fusion, and another 14% presented only one fusion. On average, 3 fusions were identified per cell-line, with a maximum of 10. Validation of some these fusion genes is required to understand their functional role. Importantly, although having IgH-or kappa-related fusions did not affect patient outcome by themselves, patients with high numbers of fusion candidates had worse event-free survival. Conclusion: Our data describes a diverse and rich fusion gene landscape in Multiple Myeloma. Similar to mutational profiles, there is no predominant fusion gene driving the disease process. Association of poor prognosis with a higher number of fusions may indicate that genomic instability plays an important role in the biology of Multiple Myeloma. Disclosures No relevant conflicts of interest to declare.
18
Yang Zepeng, 杨泽鹏, 解凯 Xie Kai, and 李桐 Li Tong. "渐进式多尺度特征级联融合颜色恒常性算法." Acta Optica Sinica 42, no. 5 (2022): 0533002. http://dx.doi.org/10.3788/aos202242.0533002.
Full text
19
Zhang Runmei, 张润梅, 毕利君 Bi Lijun, 汪方斌 Wang Fangbin, 袁彬 Yuan Bin, 罗谷安 Luo Gu'an, and 姜怀震 Jiang Huaizhen. "多尺度特征融合与锚框自适应的目标检测算法." Laser & Optoelectronics Progress 59, no. 12 (2022): 1215019. http://dx.doi.org/10.3788/lop202259.1215019.
Full text
20
Zongwen Bai, Zongwen Bai, Xiaohuan Chen Zongwen Bai, Meili Zhou Xiaohuan Chen, Tingting Yi Meili Zhou, and Wei-Che Chien Tingting Yi. "Low-rank Multimodal Fusion Algorithm Based on Context Modeling." 網際網路技術學刊 22, no. 4 (July 2021): 913–21. http://dx.doi.org/10.53106/160792642021072204018.
Full text
21
Roseline, V., and G. Heren Chellam. "A Novel Fusion Attention Algorithm for Sentimental Image Analysis." Indian Journal of Science and Technology 15, no. 9 (March 5, 2022): 386–94. http://dx.doi.org/10.17485/ijst/v15i9.2159.
Full text
22
SUN Ying, 孙颖, 侯志强 HOU Zhiqiang, 杨晨 YANG Chen, 马素刚 MA Sugang, and 范九伦 FAN Jiulun. "基于双模态融合网络的目标检测算法." ACTA PHOTONICA SINICA 52, no. 1 (2023): 0110002. http://dx.doi.org/10.3788/gzxb20235201.0110002.
Full text
23
Huang Xinxin, 黄新欣, 任永杰 Ren Yongjie, 马可瑶 Ma Keyao, and 牛志远 Niu Zhiyuan. "基于测量不确定度的视觉惯性自适应融合算法." Acta Optica Sinica 43, no. 21 (2023): 2112003. http://dx.doi.org/10.3788/aos230851.
Full text
24
Wen, Xiaodong, Xiangdong Liu, Cunhui Yu, Haoning Gao, Jing Wang, Yongji Liang, Jiangli Yu, and Yan Bai. "IOOA: A multi-strategy fusion improved Osprey Optimization Algorithm for global optimization." Electronic Research Archive 32, no. 3 (2024): 2033–74. http://dx.doi.org/10.3934/era.2024093.
Full textAbstract:
<abstract><p>With the widespread application of metaheuristic algorithms in engineering and scientific research, finding algorithms with efficient global search capabilities and precise local search performance has become a hot topic in research. The osprey optimization algorithm (OOA) was first proposed in 2023, characterized by its simple structure and strong optimization capability. However, practical tests have revealed that the OOA algorithm inevitably encounters common issues faced by metaheuristic algorithms, such as the tendency to fall into local optima and reduced population diversity in the later stages of the algorithm's iterations. To address these issues, a multi-strategy fusion improved osprey optimization algorithm is proposed (IOOA). First, the characteristics of various chaotic mappings were thoroughly explored, and the adoption of Circle chaotic mapping to replace pseudo-random numbers for population initialization improvement was proposed, increasing initial population diversity and improving the quality of initial solutions. Second, a dynamically adjustable elite guidance mechanism was proposed to dynamically adjust the position updating method according to different stages of the algorithm's iteration, ensuring the algorithm maintains good global search capabilities while significantly increasing the convergence speed of the algorithm. Lastly, a dynamic chaotic weight factor was designed and applied in the development stage of the original algorithm to enhance the algorithm's local search capability and improve the convergence accuracy of the algorithm. To fully verify the effectiveness and practical engineering applicability of the IOOA algorithm, simulation experiments were conducted using 21 benchmark test functions and the CEC-2022 benchmark functions, and the IOOA algorithm was applied to the LSTM power load forecasting problem as well as two engineering design problems. The experimental results show that the IOOA algorithm possesses outstanding global optimization performance in handling complex optimization problems and broad applicability in practical engineering applications.</p></abstract>
25
Duffy, Ellen B., and Blanca Barquera. "Membrane Topology Mapping of the Na+-Pumping NADH: Quinone Oxidoreductase from Vibrio cholerae by PhoA- Green Fluorescent Protein Fusion Analysis." Journal of Bacteriology 188, no. 24 (October 13, 2006): 8343–51. http://dx.doi.org/10.1128/jb.01383-06.
Full textAbstract:
ABSTRACT The membrane topologies of the six subunits of Na+-translocating NADH:quinone oxidoreductase (Na+-NQR) from Vibrio cholerae were determined by a combination of topology prediction algorithms and the construction of C-terminal fusions. Fusion expression vectors contained either bacterial alkaline phosphatase (phoA) or green fluorescent protein (gfp) genes as reporters of periplasmic and cytoplasmic localization, respectively. A majority of the topology prediction algorithms did not predict any transmembrane helices for NqrA. A lack of PhoA activity when fused to the C terminus of NqrA and the observed fluorescence of the green fluorescent protein C-terminal fusion confirm that this subunit is localized to the cytoplasmic side of the membrane. Analysis of four PhoA fusions for NqrB indicates that this subunit has nine transmembrane helices and that residue T236, the binding site for flavin mononucleotide (FMN), resides in the cytoplasm. Three fusions confirm that the topology of NqrC consists of two transmembrane helices with the FMN binding site at residue T225 on the cytoplasmic side. Fusion analysis of NqrD and NqrE showed almost mirror image topologies, each consisting of six transmembrane helices; the results for NqrD and NqrE are consistent with the topologies of Escherichia coli homologs YdgQ and YdgL, respectively. The NADH, flavin adenine dinucleotide, and Fe-S center binding sites of NqrF were localized to the cytoplasm. The determination of the topologies of the subunits of Na+-NQR provides valuable insights into the location of cofactors and identifies targets for mutagenesis to characterize this enzyme in more detail. The finding that all the redox cofactors are localized to the cytoplasmic side of the membrane is discussed.
26
Jiang, Lan. "Artificial Intelligence Algorithms for Multisensor Information Fusion Based on Deep Learning Algorithms." Mobile Information Systems 2022 (April 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/3356213.
Full textAbstract:
Artificial intelligence (AI) has been widely used all over the world. AI can be applied not only in mechanical learning and expert system but also in knowledge engineering and intelligent information retrieval and has achieved amazing results. This article aims to study the relevant knowledge of deep learning algorithms and multisensor information fusion and how to use deep learning algorithms and multisensor information fusion to study AI algorithms. This paper raises the question of whether the improved multisensor information fusion will affect the AI algorithm. From the data in the experiment of this article, the accuracy of the neural network before the improvement was 4.1%. With the development of society, the traditional algorithm finally dropped to 1.3%. The accuracy of the multisensor information fusion algorithm before the improvement was 3.1% at the beginning; with the development of society, it finally dropped to 1%; it can be known that the accuracy of the improved neural network is 4.6%, and with continuous improvement, it finally increased to 9.8%. The improved multisensor information fusion algorithm is the same, the accuracy at the beginning was 3.9%, and gradually increased to 9.5%. From this set of data, it can be known that the improved convolutional neural network (CNN) algorithm, and the improved multisensor information fusion algorithm should be used to study AI algorithms.
27
Morgan, Rebecca, Dulcie Keeley, E. Starr Hazard, Emma H. Allott, Bethany Wolf, Stephen J. Savage, Chanita Hughes Halbert, Sebastiano Gattoni-Celli, and Gary Hardiman. "Fusion Genes in Prostate Cancer: A Comparison in Men of African and European Descent." Biology 11, no. 5 (April 20, 2022): 625. http://dx.doi.org/10.3390/biology11050625.
Full textAbstract:
Prostate cancer is one of the most prevalent cancers worldwide, particularly affecting men living a western lifestyle and of African descent, suggesting risk factors that are genetic, environmental, and socioeconomic in nature. In the USA, African American (AA) men are disproportionately affected, on average suffering from a higher grade of the disease and at a younger age compared to men of European descent (EA). Fusion genes are chimeric products formed by the merging of two separate genes occurring as a result of chromosomal structural changes, for example, inversion or trans/cis-splicing of neighboring genes. They are known drivers of cancer and have been identified in 20% of cancers. Improvements in genomics technologies such as RNA-sequencing coupled with better algorithms for prediction of fusion genes has added to our knowledge of specific gene fusions in cancers. At present AA are underrepresented in genomic studies of prostate cancer. The primary goal of this study was to examine molecular differences in predicted fusion genes in a cohort of AA and EA men in the context of prostate cancer using computational approaches. RNA was purified from prostate tissue specimens obtained at surgery from subjects enrolled in the study. Fusion gene predictions were performed using four different fusion gene detection programs. This identified novel putative gene fusions unique to AA and suggested that the fusion gene burden was higher in AA compared to EA men.
28
Zhong, Hongye, and Jitian Xiao. "Enhancing Health Risk Prediction with Deep Learning on Big Data and Revised Fusion Node Paradigm." Scientific Programming 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/1901876.
Full textAbstract:
With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementation.
29
Luo Yujie, 罗禹杰, 张剑 Zhang Jian, 陈亮 Chen Liang, 张侣 Zhang Lü, 欧阳婉卿 Ouyang Wanqing, 黄代琴 Huang Daiqin, and 杨羽翼 Yang Yuyi. "基于自适应空间特征融合的轻量化目标检测算法." Laser & Optoelectronics Progress 59, no. 4 (2022): 0415004. http://dx.doi.org/10.3788/lop202259.0415004.
Full text
30
Pan Weihua, 潘卫华, 门媛媛 Men Yuanyuan, and 苏攀 Su Pan. "融合边缘特征的自适应滤波立体匹配算法." Laser & Optoelectronics Progress 59, no. 8 (2022): 0815010. http://dx.doi.org/10.3788/lop202259.0815010.
Full text
31
Liu Yiming, 刘一鸣, and 肖志勇 Xiao Zhiyong. "基于特征融合的肝脏肿瘤自动分割方法." Laser & Optoelectronics Progress 58, no. 14 (2021): 1417001. http://dx.doi.org/10.3788/lop202158.1417001.
Full text
32
Yang Yan, 杨燕, 武旭栋 Wu Xudong, and 杜康 Du Kang. "结合天空区域分割和加权融合的图像去雾算法." Laser & Optoelectronics Progress 58, no. 16 (2021): 1610021. http://dx.doi.org/10.3788/lop202158.1610021.
Full text
33
Xie Xiaopeng, 谢小鹏, 欧永东 Ou Yongdong, 王银安 Wang Yin'an, and 黄泽琼 Huang Zeqiong. "基于融合代价和分段优化的立体匹配算法." Laser & Optoelectronics Progress 58, no. 12 (2021): 1215004. http://dx.doi.org/10.3788/lop202158.1215004.
Full text
34
T, Yuvarju, Dileep R, Monisha Uday, and Ravikiran H K. "Comparison of Different Image Fusion Algorithm on Remote Sensed Data." International Journal of Research Publication and Reviews 4, no. 3 (March 17, 2023): 778–83. http://dx.doi.org/10.55248/gengpi.2023.4.31857.
Full text
35
Huang Yukai, 黄裕凯, 王青旺 Wang Qingwang, 沈韬 Shen Tao, 朱艳 Zhu Yan, and 宋健 Song Jian. "基于MobileNet的多尺度感受野特征融合算法." Laser & Optoelectronics Progress 60, no. 4 (2023): 0410024. http://dx.doi.org/10.3788/lop220628.
Full text
36
Yang Mengxue, 杨梦雪, 李祝莲 Li Zhulian, and 李语强 Li Yuqiang. "基于贪婪动态融合的卫星激光测距优化算法." Laser & Optoelectronics Progress 60, no. 7 (2023): 0728001. http://dx.doi.org/10.3788/lop213284.
Full text
37
Sarath, Devika. "Fusion of Underwater Images based on Tetrolet Transform &Color Correction Algorithms." Journal of Advanced Research in Dynamical and Control Systems 12, no. 01-Special Issue (February 13, 2020): 795–804. http://dx.doi.org/10.5373/jardcs/v12sp1/20201131.
Full text
38
Fan, Jiahao, and Weijun Pan. "Customization of the ASR System for ATC Speech with Improved Fusion." Aerospace 11, no. 3 (March 12, 2024): 219. http://dx.doi.org/10.3390/aerospace11030219.
Full textAbstract:
In recent years, automatic speech recognition (ASR) technology has improved significantly. However, the training process for an ASR model is complex, involving large amounts of data and a large number of algorithms. The task of training a new model for air traffic control (ATC) is considerable, as it may require many researchers for its maintenance and upgrading. In this paper, we developed an improved fusion method that can adapt the language model (LM) in ASR to the domain of air traffic control. Instead of using vocabulary in traditional fusion, this method uses the ATC instructions to improve the LM. The perplexity shows that the LM of the improved fusion is much better than that of the use of vocabulary. With vocabulary fusion, the CER in the ATC corpus decreases from 0.3493 to 0.2876. The improved fusion reduces the CER of the ATC corpora from 0.3493 to 0.2761. Although there is only a difference of less than 2% between the two fusions, the perplexity shows that the LM of the improved fusion is much better.
39
Soliman, Abanob, Hicham Hadj-Abdelkader, Fabien Bonardi, Samia Bouchafa, and Désiré Sidibé. "MAV Localization in Large-Scale Environments: A Decoupled Optimization/Filtering Approach." Sensors 23, no. 1 (January 3, 2023): 516. http://dx.doi.org/10.3390/s23010516.
Full textAbstract:
Developing new sensor fusion algorithms has become indispensable to tackle the daunting problem of GPS-aided micro aerial vehicle (MAV) localization in large-scale landscapes. Sensor fusion should guarantee high-accuracy estimation with the least amount of system delay. Towards this goal, we propose a linear optimal state estimation approach for the MAV to avoid complicated and high-latency calculations and an immediate metric-scale recovery paradigm that uses low-rate noisy GPS measurements when available. Our proposed strategy shows how the vision sensor can quickly bootstrap a pose that has been arbitrarily scaled and recovered from various drifts that affect vision-based algorithms. We can consider the camera as a “black-box” pose estimator thanks to our proposed optimization/filtering-based methodology. This maintains the sensor fusion algorithm’s computational complexity and makes it suitable for MAV’s long-term operations in expansive areas. Due to the limited global tracking and localization data from the GPS sensors, our proposal on MAV’s localization solution considers the sensor measurement uncertainty constraints under such circumstances. Extensive quantitative and qualitative analyses utilizing real-world and large-scale MAV sequences demonstrate the higher performance of our technique in comparison to most recent state-of-the-art algorithms in terms of trajectory estimation accuracy and system latency.
40
Sworder, D. D., and J. E. Boyd. "Sensor fusion in estimation algorithms." Journal of the Franklin Institute 339, no. 4-5 (July 2002): 375–85. http://dx.doi.org/10.1016/s0016-0032(02)00024-8.
Full text
41
Gabrys, Bogdan, and Dymitr Ruta. "Genetic algorithms in classifier fusion." Applied Soft Computing 6, no. 4 (August 2006): 337–47. http://dx.doi.org/10.1016/j.asoc.2005.11.001.
Full text
42
LIPOVETSKY, STAN. "DATA FUSION IN SEVERAL ALGORITHMS." Advances in Adaptive Data Analysis 05, no. 03 (July 2013): 1350014. http://dx.doi.org/10.1142/s1793536913500143.
Full textAbstract:
Data fusion consists of the process of integrating several datasets with some common variables, and other variables available only in partial datasets. The main problem of data fusion can be described as follows. From one source, having X0 and Y0 datasets (with N0 observations by multiple x and y variables, n and m of those, respectively), and from another source, having X1 data (with N1 observations by the same nx-variables), we need to estimate the missing portion of the Y1 data (of size N1 by m variables) in order to combine all the data into one set. Several algorithms are considered in this work, including estimation of weights proportional to the distances from each ith observation in the X1 "recipients" dataset to all observations in the X0 "donors" dataset. Or we can use a sample balancing technique with the maximum effective base performed by applying ridge-regression for the Gifi system of binaries obtained from the x-variables for the best fit of the "donors" X0 data to the margins defined by each respondent in the "recipients" X1 dataset. Then the weighted regressions of each y in the Y0 dataset by all variables in the X0 are constructed. For each ith observation in the dataset X0, these regressions are used for predicting the y-variables in the Y1 "recipients" dataset. If X and Y are the same n variables from different sources, the dual partial least squares technique and a special regression model with dummies defining each of the three available sets are used for prediction of the Y1 data.
43
Zhang, Xiucai, Lei He, Junyi Chen, Baoyun Wang, Yuhai Wang, and Yuanle Zhou. "Multiattention Mechanism 3D Object Detection Algorithm Based on RGB and LiDAR Fusion for Intelligent Driving." Sensors 23, no. 21 (October 26, 2023): 8732. http://dx.doi.org/10.3390/s23218732.
Full textAbstract:
This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm’s performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.
44
Fisher, Kevin E., Jacquelyn Reuther, Hadi Sayeed, Erica Fang Tam, Vijetha Kumar, Lizmery Suarez Ferguson, Jyotinder Nain Punia, et al. "Leukemia Fusion Gene Detection in the Clinical Molecular Laboratory Using RNA-Based Targeted Next-Generation Sequencing." Blood 128, no. 22 (December 2, 2016): 4074. http://dx.doi.org/10.1182/blood.v128.22.4074.4074.
Full textAbstract:
Abstract Introduction: The diagnosis and risk stratification for patients with B-lymphoblastic leukemia (B-ALL) requires the accurate detection of fusion genes such as BCR-ABL1, ETV6-RUNX1,or Philadelphia-like (Ph-like) B-ALL kinase fusions, or gene rearrangements (e.g. KMT2A). No single assay can detect all relevant alterations, so costly and inefficient testing algorithms that combine karyotyping, fluorescent in situ hybridization (FISH), and reverse transcriptase PCR (RT-PCR) are often required. A comprehensive and sensitive RNA-based next-generation sequencing (NGS) assay that could consolidate diagnostic and prognostic B-ALL gene fusion and rearrangement testing into a single clinical test is an attractive alternative. Methods: We obtained RNA from 15 clinical specimens collected from 14 patients [11 bloods (2 from the same patient) and 4 bone marrows] with hematologic malignancies and known genomic alterations by RT-PCR, FISH, or cytogenetics, and 1 Ph-positive B-ALL cell line (SUP-B15). Samples harbored either B-ALL-associated alterations [BCR-ABL1 (3), ETV6-RUNX1 (2), intrachromosomal amplification 21 (iAMP21) (2), and KMT2A (2) or PDGFRB (1) rearrangements], or alterations detected in other hematologic malignancies [PML-RARA (2, same patient), RUNX1-RUNX1T1, t(5;9)(q33;q22), t(1;4)(p13;q12), and monosomy 7]. We prepared NGS libraries from extracted total RNA (100 ng) using the Archer® FusionPlex® Heme v2 anchored multiplex PCR-based NGS library with molecular barcoding protocol that targets 553 exons in 87 genes associated with B-ALL and other hematologic malignancies for detection of expressed fusions regardless of gene partner. Illumina paired-end indexed libraries were multiplexed (8 per run) and sequenced on a MiSeq (2x150 bp, v2) in two separate runs. Data were analyzed using vendor-provided virtual-machine based analysis pipelines and custom-developed scripts. ANNOVAR was used for breakpoint annotation, and fusion calls were compared to previous molecular results. Results: A mean total of 5.1x105 unique and 6.26x104 unique RNA paired-end reads were generated. One sample [t(1;4)(p13;q12)] failed QC due to inadequate RNA reads and was excluded from analysis. NGS detected the fusion or gene rearrangement in 10/11 samples (91%) with mean supporting split read counts of 175 reads (range: 6-1209). NGS detected both ETV6-RUNX1 fusions in samples with 4% and 5% blasts, respectively, defined binding partners for the KMT2A and PDGFRB gene rearrangements (KMT2A-MLLT10, KMT2A-MLLT3, and CCDC88C-PDGFRB), and detected novel P2RY8-CRLF2 fusions in both iAMP21 samples. NGS failed to detect a PML-RARA fusion in a post-treatment low-level RT-PCR-positive, FISH-, flow-, and morphology-negative blood sample, and no fusion was detected in the t(5;9)(q33;q22) sample. A low-level KMT2A-MLLT10 fusion was detected in an ETV6-RUNX1 sample suggesting a minor subclone or false-positive result. Conlcusions: In summary, RNA-based targeted NGS detects gene fusions and rearrangements comparable to conventional methods, accurately detects fusions in samples with ~5% blasts, and highlights previously unknown fusions and fusion partners. Additional studies are required to establish clinical specimen requirements, limit of detection, sensitivity, and specificity. Experiments are underway to confirm novel fusions, sequence additional recurrent B-ALL alterations (e.g. JAK2, TCF3, and PDGFRA fusions), and assess assay performance in formalin-fixed, paraffin-embedded tissue samples. RNA-based targeted NGS may be a clinically useful method to detect gene fusions and rearrangements in B-ALL and other hematologic malignancies. Disclosures No relevant conflicts of interest to declare.
45
Chen, Xiangbing, and Jie Zhou. "Multisensor Estimation Fusion on Statistical Manifold." Entropy 24, no. 12 (December 9, 2022): 1802. http://dx.doi.org/10.3390/e24121802.
Full textAbstract:
In the paper, we characterize local estimates from multiple distributed sensors as posterior probability densities, which are assumed to belong to a common parametric family. Adopting the information-geometric viewpoint, we consider such family as a Riemannian manifold endowed with the Fisher metric, and then formulate the fused density as an informative barycenter through minimizing the sum of its geodesic distances to all local posterior densities. Under the assumption of multivariate elliptical distribution (MED), two fusion methods are developed by using the minimal Manhattan distance instead of the geodesic distance on the manifold of MEDs, which both have the same mean estimation fusion, but different covariance estimation fusions. One obtains the fused covariance estimate by a robust fixed point iterative algorithm with theoretical convergence, and the other provides an explicit expression for the fused covariance estimate. At different heavy-tailed levels, the fusion results of two local estimates for a static target display that the two methods achieve a better approximate of the informative barycenter than some existing fusion methods. An application to distributed estimation fusion for dynamic systems with heavy-tailed process and observation noises is provided to demonstrate the performance of the two proposed fusion algorithms.
46
Kerbs, Paul, Aarif Mohamed Nazeer Batcha, Sebastian Vosberg, Dirk Metzler, Tobias Herold, and Philipp A. Greif. "Gene Fusion Detection By RNA-Seq in Acute Myeloid Leukemia (AML)." Blood 134, Supplement_1 (November 13, 2019): 4655. http://dx.doi.org/10.1182/blood-2019-125869.
Full textAbstract:
Accurate and complete genetic classification of AML is crucial for the prediction of clinical outcome and treatment stratification. Deciphering the spectrum of genetic abnormalities by polymerase chain reaction (PCR), karyotyping and fluorescence in situ hybridization (FISH) in routine diagnostics is the current gold standard, however, fusion genes might potentially be missed by these assays. Recently, several methods have been developed to improve the detection of gene fusion transcripts based on RNA sequencing data, providing robust results. To test the detection power and assess the applicability of RNA-Seq based methods in clinical diagnostics we applied two different algorithms, namely FusionCatcher (Nicorici D et al., bioRxiv, 2014) and Arriba (Uhrig S et al., DKFZ, https://github.com/suhrig/arriba), to the transcriptomes of 895 well-characterized AML samples from three independently sequenced cohorts: AMLCG (Herold T et al., Haematologica, 2018, n=261), DKTK (Greif PA et al., Clin Cancer Res, 2018 and unpublished data, n=166), BeatAML (Tyner JW et al., Nature 2018, n=468) and publicly available healthy control samples (SRA studies: SRP018028, SRP047126, SRP050146, SRP105369, SRP115911, SRP133442, n=38). According to karyotyping, 31% (277/895) of samples harbored chromosomal aberrations putatively causing gene fusions (i.e. translocations, interstitial deletions, duplications, inversions, insertions). Analyses by FISH and/or PCR confirmed these rearrangements in 51.3% (142/277) of samples, whereas fusion detection by the means of RNA-Seq showed evidence for fusion genes corresponding to these rearrangements in 60.3% (167/277) of samples. Chromosomal aberrations, identified by karyotyping, which are known to result in clinically relevant fusions (e.g. RUNX1-RUNX1T1, KMT2A fusions) were confirmed by FISH/PCR (AMLCG: n=27/27, DKTK: n=21/21, BeatAML: n=54/57) and RNA-Seq based methods (AMLCG: n=17/27, DKTK: n=21/21, BeatAML: n=56/57) in most of the cases. Of note, the AMLCG cohort was sequenced using the SENSE mRNA Library Prep Kit from Lexogen which seems to be not optimal for fusion detection. Furthermore, 19 samples (AMLCG: n=12, DKTK: n=4, BeatAML: n=3) were found to harbor known pathogenic fusions, described in previous studies, which were not reported by routine diagnostics: NUP98-NSD1 (n=11); CBFB-MYH11, RUNX1-RUNX1T1 and DEK-NUP214 (n=2 each); RUNX1-CBFA2T2 and RUNX1-CBFA2T3 (n=1 each). Reanalysis of six of these samples by PCR confirmed three fusions which were initially missed by routine diagnostics. In general, the amount of reported fusion events by RNA-Seq is high (on average 69 and 39 per sample as detected by FusionCatcher and Arriba respectively), even after applying the built-in filters, indicating a high false positive rate. To robustly identify putative novel fusions, we developed a filtering pipeline and incorporated two new filtering steps. The promiscuity score (PS) of a fusion measures the amount of further distinct fusion partners which were detected in the respective cohort for the 5' and 3' gene. The fusion transcript score (FTS) measures the relative abundance of a fusion transcript to its 5' and 3' partner gene. PS and FTS of known, clinically relevant fusions confirmed by FISH/PCR were used to define cut-offs. To further maximize specificity while maintaining sensitivity, we excluded fusion events which we detected in publicly available healthy samples and subsequently filtered for overlapping calls from FusionCatcher and Arriba (Fig. 1A). Additionally, we obtained further evidence for a fusion event by an elevated transcription of the 3' fusion partner. In case of a fusion event, the transcription of the 3' partner gene likely gets under the control of the promoter of the 5' partner gene. This results in an elevated transcription of genes which are otherwise transcribed at low levels (Fig. 1B-C). Thus, we identified five putatively novel recurrent fusion genes which were detected in two cohorts independently: NRIP1-MIR99AHG, LATS2-ZMYM2, ATP11A-ING1, MBP-SLC66A2, PRDM16-SKI (Fig. 1D-F). Although these events were called with high evidence, we aim at independent validation by complementary methods. In our study, we have not only demonstrated that the application of RNA-Seq to the detection of fusion genes is a valuable complement to diagnostic routine but also has the potential to discover novel putatively pathogenic fusions. Disclosures No relevant conflicts of interest to declare.
47
Hui Guancheng, 惠冠程, 李开放 Li Kaifang, 辛明 Xin Ming, and 张苗辉 Zhang Miaohui. "基于视频行人重识别和时空特征融合的跟踪算法." Laser & Optoelectronics Progress 59, no. 12 (2022): 1215004. http://dx.doi.org/10.3788/lop202259.1215004.
Full text
48
Shao Xinbo, 邵新博, 苑自勇 Yuan Ziyong, 刘夏林 Liu Xialin, and 舒嵘 Shu Rong. "基于运动信息融合的高精度超前瞄准角算法." Acta Optica Sinica 42, no. 18 (2022): 1812003. http://dx.doi.org/10.3788/aos202242.1812003.
Full text
49
Yang Yanchun, 杨艳春, 高晓宇 Gao Xiaoyu, 党建武 Dang Jianwu, and 王阳萍 Wang Yangping. "基于NSST与IFCNN的红外可见光图像融合算法." Laser & Optoelectronics Progress 58, no. 20 (2021): 2010004. http://dx.doi.org/10.3788/lop202158.2010004.
Full text
50
GUO Lulu, 郭露露, and 易红伟 YI Hongwei. "基于局域加权叠加的高动态范围图像融合算法." ACTA PHOTONICA SINICA 51, no. 11 (2022): 1110001. http://dx.doi.org/10.3788/gzxb20225111.1110001.
Full text