Article

omicML: An Integrative Bioinformatics and Machine Learning Framework for Transcriptomic Biomarker Identification

2025-10-27

Abstract excerpt

<h4>Introduction</h4> Transcriptomic biomarker discovery has been a challenge due to variation in datasets and platforms, complexity in statistical and computational methods, integration of multiple programming languages, and intricacy of ML workflow to evaluate biomarkers. Standard workflows necessitate several stages (quality control, normalization, differential expression), typically executed in R or Python, r...

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Literature Corpus work: 6acea01c-438e-56ea-b0be-2f67987f781b
DOI: 10.1101/2025.10.25.684517

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