An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.
Journal:
BMC bioinformatics
Published Date:
Sep 18, 2023
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.