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

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 popul...

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Bibliographic Details
Main Authors: Troy M. LaPolice, Yi-Fei Huang
Format: Article
Language:English
Published: BMC 2023-09-01
Series:BMC Bioinformatics
Subjects:
Deep Learning
Unsupervised
Essential Genes
Loss of Function Intolerance
Population Genomics
Functional Genomics
Online Access:https://doi.org/10.1186/s12859-023-05481-z

Internet

https://doi.org/10.1186/s12859-023-05481-z

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