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Article

A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus.

PloS one - 1 Jan 2020

Haga Hiroaki, Sato Hidenori, Koseki Ayumi, Saito Takafumi, Okumoto Kazuo, Hoshikawa Kyoko, Katsumi Tomohiro, Mizuno Kei, Nishina Taketo, Ueno Yoshiyuki

Abstract excerpt

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 33152046
DOI: 10.1371/journal.pone.0242028
PMCID: PMC7644079

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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