3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints | Zendy

Dhonggun Won | Zendy; Dongwook Kim | Zendy; Junwoo Woo | Zendy; Kyoungyeul Lee | Zendy

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3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints

Author(s) -

Dhonggun Won,

Dongwook Kim,

Junwoo Woo,

Kyoungyeul Lee

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab529

Subject(s) - overfitting , computer science , pathogenicity , machine learning , context (archaeology) , artificial intelligence , artificial neural network , human genome , genome , biology , genetics , gene , paleontology , microbiology and biotechnology

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.

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