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ATMeQ: A Machine Learning-Based Framework for Amyotrophic Lateral Sclerosis Disease using RNA-seq Meta-Analysis

2026-04-17

Abstract excerpt

<title>Abstract</title> <p>methods Random Forest importance, Gradient Boosting, Recursive Feature Elimination (RFE), and the Boruta algorithm, narrowed this set down to a biologically meaningful six-gene signature (ACTA1, ABCA4, COL6A4P2, HERC2P2, KCNE4, LOC107987008). Employing this signature, fifteen machine learning models were trained and optimized through hyperparameter tuning. The top-performing model, a G...

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Literature Corpus work: 81c70620-f7b1-59be-91a3-10a3276cbd74
DOI: 10.21203/rs.3.rs-8614090/v1

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