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3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints
- Source :
- Bioinformatics
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
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Abstract
- Motivation Improvements in next-generation sequencing have enabled genome-based diagnosis for patients with genetic diseases. However, accurate interpretation of human variants requires knowledge from a number of clinical cases. In addition, manual analysis of each variant detected in a patient's genome requires enormous time and effort. To reduce the cost of diagnosis, various computational tools have been developed to predict the pathogenicity of human variants, but the shortage and bias of available clinical data can lead to overfitting of algorithms. Results We developed a pathogenicity predictor, 3Cnet, that uses recurrent neural networks to analyze the amino acid context of human variants. As 3Cnet is trained on simulated variants reflecting evolutionary conservation and clinical data, it can find disease-causing variants in patient genomes with 2.2 times greater sensitivity than currently available tools, more effectively discovering pathogenic variants and thereby improving diagnosis rates. Availability and implementation Codes (https://github.com/KyoungYeulLee/3Cnet/) and data (https://zenodo.org/record/4716879#.YIO-xqkzZH1) are freely available to non-commercial users. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- Statistics and Probability
AcademicSubjects/SCI01060
Computer science
Multi-task learning
Economic shortage
Context (language use)
Computational biology
Overfitting
Genome Analysis
Pathogenicity
Original Papers
Biochemistry
Genome
Computer Science Applications
Computational Mathematics
Recurrent neural network
Computational Theory and Mathematics
In patient
Molecular Biology
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....7a5750ae69279fb628fc78b3c3fe1f47