ArticleBMC bioinformatics2017
Unsupervised gene selection using biological knowledge : application in sample clustering.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 33 citations in OpenAlex.
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Biologically weighted LASSO: enhancing functional interpretability in gene expression data analysis.Bioinformatics (Oxford, England) · 2024Article
- CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis.PeerJ. Computer science · 2021Article
- Multi-view feature selection for identifying gene markers: a diversified biological data driven approach.BMC bioinformatics · 2020Article
- Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data.Entropy (Basel, Switzerland) · 2020Review
- A consensus multi-view multi-objective gene selection approach for improved sample classification.BMC bioinformatics · 2020Article
- A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.Scientific reports · 2020Article
- Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions.Frontiers in genetics · 2020Review
- Comparative Transcriptomics of the Bovine Apicomplexan ParasiteFrontiers in veterinary science · 2020Article
- Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches.Journal of integrative bioinformatics · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClassification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role.
resultsThe current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of gene selection. At the end, a multi-objective clustering approach is deployed to cluster the available set of samples in the reduced gene space.
conclusionsReported results show that consideration of biological knowledge in gene selection technique not only reduces the feature space dimensionality in great extent but also improves the accuracy of sample classification. The obtained reduced gene space is validated using strong biological significance tests. In order to prove the supremacy of our proposed gene selection based sample clustering technique, a thorough comparative analysis has also been performed with state-of-the-art techniques.
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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.