ArticleBMC bioinformatics2018
Feature selection of gene expression data for Cancer classification using double RBF-kernels.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Efficient Techniques Based on Sparse Representation for Classifying High-dimensional Multiclass Microarray Data.Journal of medical signals and sensors · 2026Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification.IEEE access : practical innovations, open solutions · 2025Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Synovial fibroblast gene expression is associated with sensory nerve growth and pain in rheumatoid arthritis.Science translational medicine · 2024Article
- Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.BMC bioinformatics · 2024Article
- Development and evaluation of a chronic kidney disease risk prediction model using random forest.Frontiers in genetics · 2024Article
- Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm.Cancers · 2022Article
- Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study.International journal of molecular sciences · 2022Article
- Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts.Scientific reports · 2022Article
- EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.Entropy (Basel, Switzerland) · 2022Article
- GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints.Cancers · 2022Article
- Identifying common transcriptome signatures of cancer by interpreting deep learning models.Genome biology · 2022Article
- The ability to classify patients based on gene-expression data varies by algorithm and performance metric.PLoS computational biology · 2022Article
- A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma.PloS one · 2022Article
- Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer.Scientific reports · 2021Article
- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants.BMC bioinformatics · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundUsing knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem.
resultsThe modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime.
conclusionsThis paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.
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