ArticleProceedings of the National Academy of Sciences of the United States of America2000
Knowledge-based analysis of microarray gene expression data by using support vector machines.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 414 papers, 4 of them syntheses that pooled it.
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Who cites it
414 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.Scientific reports · 2023Pooled it
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- Breast cancer stage prediction: a computational approach guided by transcriptome analysis.Molecular genetics and genomics : MGG · 2022Pooled it
- Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.Molecular medicine reports · 2018Pooled it
- Post-translational modifications in metabolic reprogramming: implications for metabolic therapy and immunotherapy in cancer.Signal transduction and targeted therapy · 2026Review
- Predicting functions of uncharacterized gene products from microbial communities.Nature biotechnology · 2026Article
- Predictive modeling and network analysis uncover novel regulators of multi-stress tolerance in Saccharomyces cerevisiae.BMC microbiology · 2026Article
- Deep learning and attention mechanisms to identify key genes and their implications for the origin of insect wings.Scientific reports · 2026Article
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- Unveiling Shared Immune Responses in Porcine Alveolar Macrophages during ASFV and PRRSV Infection Using Single-Cell RNA-seq.Microorganisms · 2024Article
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- Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.Frontiers in bioinformatics · 2024Article
- Article
354 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.
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Registered trials
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.