ArticlePloS one2017
Accurate and fast feature selection workflow for high-dimensional omics data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- moiraine: an R package to construct reproducible pipelines for the application and comparison of multi-omics integration methods.Bioinformatics (Oxford, England) · 2026Article
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).Scientific reports · 2024Article
- Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.Cardiovascular diagnosis and therapy · 2023Review
- Dynamic regional homogeneity alterations and cognitive impairment in patients with moderate and severe obstructive sleep apnea.Frontiers in neuroscience · 2022Article
- Bluster or Lustre: Can AI Improve Crops and Plant Health?Plants (Basel, Switzerland) · 2021Review
- Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data.BMC bioinformatics · 2021Article
- Drug design by machine-trained elastic networks: predicting Ser/Thr-protein kinase inhibitors' activities.Molecular diversity · 2021Article
- Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells.Cell reports · 2021Article
- Molecular Diversity of Clinically Stable Human Kidney Allografts.JAMA network open · 2021Article
- Article
- Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis.Frontiers in immunology · 2021Article
- Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research.Frontiers in genetics · 2021Article
- Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.Scientific reports · 2020Article
- The omics discovery REST interface.Nucleic acids research · 2020Article
- The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination.Bioinformatics (Oxford, England) · 2020Article
- GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets.BMC bioinformatics · 2020Article
- Multi-omics Data Integration, Interpretation, and Its Application.Bioinformatics and biology insights · 2020Review
- A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.Expert systems with applications · 2019Article
- Wx: a neural network-based feature selection algorithm for transcriptomic data.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We are moving into the age of 'Big Data' in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S * F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical omics classification includes redundant and irrelevant features (e.g. genes or proteins) that can result in long computation times; decrease of the model performance and the selection of suboptimal features (genes and proteins) after the classification/regression step. Multiple algorithms and reviews has been published to describe all the existing methods for feature selection, their strengths and weakness. However, the selection of the correct FS algorithm and strategy constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a 'grey zone'. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domain's problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.
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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.