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

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 ge...

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Detalles Bibliográficos
Autores principales: LaPolice, Troy M., Huang, Yi-Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506225/
https://www.ncbi.nlm.nih.gov/pubmed/37723435
http://dx.doi.org/10.1186/s12859-023-05481-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506225/
https://www.ncbi.nlm.nih.gov/pubmed/37723435
http://dx.doi.org/10.1186/s12859-023-05481-z

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