An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates

An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates

Hepatitis C virus (HCV) infection affects over 71 million people worldwide, leading to severe liver diseases, including cirrhosis and hepatocellular carcinoma. The virus’s high mutation rate complicates current antiviral therapies by promoting drug resistance, emphasizing the need for novel therapeu...

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Bibliographic Details
Main Authors: Teuku Rizky Noviandy, Aga Maulana, Ghifari Maulana Idroes, Rivansyah Suhendra, Razief Perucha Fauzie Afidh, Rinaldi Idroes
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sci
Subjects:
hepatitis C virus
machine learning
explainability
drug discovery
Online Access:https://www.mdpi.com/2413-4155/6/4/81
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