An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.

ABSTRACT:

Background

The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed to predict human essential genes from population genomic data. While the existing methods are highly predictive of essential genes of long length, they have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome.

Results

Motivated by the premise that population and functional genomic data may provide complementary evidence for gene essentiality, here we present an evolution-based deep learning model, DeepLOF, to predict essential genes in an unsupervised manner. Unlike previous p

SUBMITTER: LaPolice TM 

PROVIDER: S-EPMC10506225 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature