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Identification of early liver toxicity gene biomarkers using comparative supervised machine learning

Medicine and Health

Identification of early liver toxicity gene biomarkers using comparative supervised machine learning

B. P. Smith, L. S. Auvil, et al.

This groundbreaking research by Brandi Patrice Smith, Loretta Sue Auvil, Michael Welge, Colleen Bannon Bushell, Rohit Bhargava, Navin Elango, Kamin Johnson, and Zeynep Madak-Erdogan identifies early exposure gene signatures for liver toxicity using advanced machine learning techniques. The study discovered ten high-accuracy gene biomarkers which could revolutionize and expedite toxicity testing for agrochemicals and pharmaceuticals.

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Playback language: English
Abstract
Screening agrochemicals and pharmaceuticals for liver toxicity is expensive and time-consuming. This study uses comparative supervised machine learning on the rat liver TG-GATEs dataset to identify early exposure gene signatures and predictive models. Ten gene biomarkers were identified using three feature selection methods, predicting liver necrosis with high accuracy in an independent validation dataset (MAQC-II). Nine of these genes are involved in metabolism and detoxification, and one in transcriptional regulation. Several are also implicated in liver carcinogenesis. This biomarker gene signature offers high accuracy and could accelerate toxicity testing.
Publisher
Scientific Reports
Published On
Nov 05, 2020
Authors
Brandi Patrice Smith, Loretta Sue Auvil, Michael Welge, Colleen Bannon Bushell, Rohit Bhargava, Navin Elango, Kamin Johnson, Zeynep Madak-Erdogan
Tags
liver toxicity
machine learning
biomarkers
agrochemicals
pharmaceuticals
gene signatures
toxicity testing

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