Article

MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data

2024-07-24

Abstract excerpt

MultiOmicsAgent (MOAgent) is an innovative, Python based open-source tool for biomarker discovery, utilizing machine learning techniques specifically extreme gradient-boosted decision trees to process multi-omics data. With its cross-platform compatibility, user-oriented graphical interface and a well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning bu...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f472904a-6e39-51dd-9b5e-851dddc55cc6
DOI: 10.1101/2024.07.24.604727

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics dataDOI 10.1101/2024.07.24.604727Read

Transparent exploration of machine learning for biomarker discovery from proteomics and omics dataSemantic proximity Read

playOmics: A multi-omics pipeline for interpretable predictions and biomarker discoverySemantic proximity Read

BiomarkerML: A cloud-based proteomics ML workflow for biomarker discoverySemantic proximity Read

omicML: An Integrative Bioinformatics and Machine Learning Framework for Transcriptomic Biomarker IdentificationSemantic proximity Read

OmicSelector: automatic feature selection and deep learning modeling for omic experimentsSemantic proximity Read

Just Add Data: Automated Predictive Modeling and BioSignature DiscoverySemantic proximity Read

Deep learning–driven multi-omics analysis: enhancing cancer diagnostics and therapeuticsSemantic proximity Read

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinderSemantic proximity Read

A new pipeline for cross-validation fold-aware machine learning prediction of clinical outcomes addresses hidden data-leakage in omics based ‘predictors’Semantic proximity Read

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer DiagnosticsSemantic proximity Read

DERMA-Agent: An Agentic Framework for Prognostic Discovery in Pan-Cancer PathologySemantic proximity Read

Sparse, trainable subnetworks for multi-omics integration: a cross-validated evaluation of the Lottery Ticket Hypothesis across nutrigenomic, toxicogenomic, and oncogenomic datasetsSemantic proximity Read