ArticleJournal of biomedical informatics2018
Benchmarking relief-based feature selection methods for bioinformatics data mining.
Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
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- Altered EEG microstate dynamics in adolescents with non-suicidal self-injury at rest and following acute social exclusion.Frontiers in psychiatry · 2026Article
- Survival-LCS: Rule-Based Survival Analysis without Proportional Hazard Assumptions.ACM transactions on evolutionary learning and optimization · 2026Article
- Phase-Alternation and Tree-Initialization to Facilitate Interpretable Rule-based Machine Learning.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2026Article
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach.Journal of neuroengineering and rehabilitation · 2025Article
- Integrated bioinformatic and in vivo analysis confirms the cardioprotective role of OPA1.BMC cardiovascular disorders · 2025Article
- Performance of stacking machine learning and volume model for improving corn above ground biomass prediction.Plant phenomics (Washington, D.C.) · 2025Article
- Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction.Cancers · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications.Life (Basel, Switzerland) · 2025Article
- Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.Nature communications · 2025Article
- Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling.Genetic and Evolutionary Computation Conference : [proceedings]. Genetic and Evolutionary Computation Conference · 2025Article
- AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.Scientific reports · 2024Article
- Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device.Brain sciences · 2024Article
- Assessing the limitations of relief-based algorithms in detecting higher-order interactions.BioData mining · 2024Article
- Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort.Cancer medicine · 2024Article
- Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study.Cancers · 2024Article
22 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. 'omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e.g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e.g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable. To that end, this work examines a set of filter-style feature selection algorithms inspired by the 'Relief' algorithm, i.e. Relief-Based algorithms (RBAs). We implement and expand these RBAs in an open source framework called ReBATE (Relief-Based Algorithm Training Environment). We apply a comprehensive genetic simulation study comparing existing RBAs, a proposed RBA called MultiSURF, and other established feature selection methods, over a variety of problems. The results of this study (1) support the assertion that RBAs are particularly flexible, efficient, and powerful feature selection methods that differentiate relevant features having univariate, multivariate, epistatic, or heterogeneous associations, (2) confirm the efficacy of expansions for classification vs. regression, discrete vs. continuous features, missing data, multiple classes, or class imbalance, (3) identify previously unknown limitations of specific RBAs, and (4) suggest that while MultiSURF
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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.