Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling

Abstract Background Deep mutational scanning (DMS) provides comprehensive maps of protein variant effects but remains experimentally intensive. Machine learning (ML) approaches have the potential to reduce experimental burden of DMS by predicting the functional impact of substitutions from limited d...

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Bibliographic Details

Published in:	BMC Bioinformatics
Main Authors:	Emily Morgan, Shaylyn Govender, Prashant Singh, Ian Goodfellow, Stephen C. Graham, Nigel T. Bishop, Özlem Tastan Bishop
Format:	Article
Language:	English
Published:	BMC 2026-05-01
Subjects:	Brute-force approach Deep mutational scanning Random Forest Support vector machine Zero-shot prediction
Online Access:	https://doi.org/10.1186/s12859-026-06473-5