ArticleFrontiers in genetics2019
Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Benchmarking large language models for cell-free RNA diagnostic biomarker discovery.Nature communications · 2026Article
- Plasma cell-free transcriptome profiling in blood plasma from chronic liver disease patients.Scientific data · 2026Article
- Embryonic Hormetic Priming Modulates Later-Life Thermal Tolerance.Ecology and evolution · 2026Article
- ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.Clinical proteomics · 2026Article
- Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.Foods (Basel, Switzerland) · 2025Review
- Deciphering the molecular basis of skin color variation through transcriptomics and machine learning.Scientific reports · 2025Article
- RnaXtract, a tool for extracting gene expression, variants, and cell-type composition from bulk RNA sequencing.Scientific reports · 2025Article
- Review
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- BAMBI integrates biostatistical and artificial intelligence methods to improve RNA biomarker discovery.Briefings in bioinformatics · 2025Article
- ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics.Journal of proteome research · 2025Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Article
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants.International journal of molecular sciences · 2024Article
- SARS-CoV-2 infection modifies the transcriptome of the megakaryocytes in the bone marrow.Blood advances · 2024Article
- Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.Clinical and translational science · 2024Article
- Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.BMC bioinformatics · 2024Article
- Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network.Frontiers in bioinformatics · 2024Article
- Pearl shape classification using deep convolutional neural networks from Tahitian pearl rotation in Pinctada margaritifera.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.