ArticleComputational and mathematical methods in medicine2018
Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System.Entropy (Basel, Switzerland) · 2021Article
- An Amalgamated Approach to Bilevel Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Colon Cancer.BioMed research international · 2020Article
- An Attribute Reduction Method Using Neighborhood Entropy Measures in Neighborhood Rough Sets.Entropy (Basel, Switzerland) · 2019Article
- Feature Genes Selection Using Fuzzy Rough Uncertainty Metric for Tumor Diagnosis.Computational and mathematical methods in medicine · 2019Article
- Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods.Medical & biological engineering & computing · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC
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Registered trials
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