An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

Abstract Background and objective Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessment...

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Bibliographic Details
Main Authors: Subrata Das, Vatsal Patel, Shouvik Chakravarty, Arnab Ghosh, Anirban Mukhopadhyay, Nidhan K. Biswas
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BioData Mining
Subjects:
Driver mutation
Machine learning
Pathogenecity prediction algorithm
Online Access:https://doi.org/10.1186/s13040-024-00420-x
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