A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations.

ABSTRACT:

Background

Understanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data.

Results

We prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to

SUBMITTER: Jia H 

PROVIDER: S-EPMC8171027 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature