Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set

Stein, David, Kars, Meltem Ece, Wu, Yiming, Bayrak, Çiğdem Sevim, Stenson, Peter D., Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484, Schlessinger, Avner and Itan, Yuval 2023. Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of a diverse feature set. Genome Medicine 15 (1) , 103. 10.1186/s13073-023-01261-9

PDF - Published Version Download (5MB)

Abstract

Gain-of-function (GOF) variants give rise to increased/novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. Experimental approaches for identifying GOF and LOF are generally slow and costly, whilst available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, a machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants, trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics. LoGoFunc outperforms other tools trained solely to predict pathogenicity for identifying pathogenic GOF and LOF variants and is available at https://itanlab.shinyapps.io/goflof/.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	BioMed Central
Date of First Compliant Deposit:	1 December 2023
Date of Acceptance:	16 November 2023
Last Modified:	01 Dec 2023 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/164469

Actions (repository staff only)

Edit Item