Minimum Redundancy Maximum Relevance Feature Selection-Application on Single-Cell RNA Sequencing Dataset.

Mirto M Gasparinatou, George Dimitrakopoulos, Aristidis Vrahatis, Panagiotis Vlamos

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Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Mirto M GasparinatouBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.ORCID https://orcid.org/0009-0008-6934-6022

George DimitrakopoulosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.ORCID https://orcid.org/0000-0002-7817-9552

Aristidis VrahatisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.ORCID https://orcid.org/0000-0003-1892-0000

Panagiotis VlamosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.ORCID https://orcid.org/0000-0003-0053-7847

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Variable selection is crucial in statistical problems involving functional data, as it enhances prediction accuracy by filtering out irrelevant features. While selecting the most relevant variables is important, focusing solely on relevance can result in redundancy, negatively affecting model efficiency. The minimum Redundancy Maximum Relevance (mRMR) method addresses this by balancing relevance and redundancy using mutual information to evaluate variable relationships. Herein, we evaluate the mRMR method on a dataset of a single-cell RNA sequencing from Parkinson's disease (PD) patients. We analyze and compare two different classification algorithms in terms of their performance in predicting the target variable and the computational time required. Additionally, we investigate the performance of these algorithms on the same dataset without feature selection, analyzing and comparing the results.

Indexed as

AlgorithmsParkinson DiseaseSequence Analysis, RNASingle-Cell AnalysisComputational BiologyHumansClassification algorithmsFeature selectionmRMR

Identifiers

PMID41273564

DOI10.1038/srep38318

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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.