A reproducible, interpretable machine learning framework for multi-omics research — bridging biology and scalable ML.
OmicML is a research-focused platform that integrates multi-omics datasets with robust machine learning pipelines to provide reproducible analyses and interpretable insights. The platform emphasizes best practices in data preprocessing, model explainability, and experiment management to accelerate translational research.
Core capabilities that help researchers build, test, and explain models.
Supports genomics, transcriptomics and proteomics data ingestion with built-in validators.
Detailed information can go here — for example, configuration options, supported data formats, example commands, or links to the paper and code repository.
Explainable ML pipelines (feature importance, SHAP summaries) designed for researchers.
Detailed information can go here — for example, configuration options, supported data formats, example commands, or links to the paper and code repository.
Containerized experiment exports and easy environment snapshots for reproducibility.
Detailed information can go here — for example, configuration options, supported data formats, example commands, or links to the paper and code repository.
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Supervisors of OmicML.
Research students and contributors behind OmicML.
