{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360011143710115712.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1142/s0219720005001004"}},{"identifier":{"@type":"URI","@value":"https://www.worldscientific.com/doi/pdf/10.1142/S0219720005001004"}}],"dc:title":[{"@value":"MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p> How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy — maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naïve Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. </jats:p><jats:p> Supplimentary: The top 60 MRMR genes for each of the datasets are listed in . More information related to MRMR methods can be found at . </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380011143710115712","@type":"Researcher","foaf:name":[{"@value":"CHRIS DING"}],"jpcoar:affiliationName":[{"@value":"Computational Research Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, 94720, USA"}]},{"@id":"https://cir.nii.ac.jp/crid/1380011143710115713","@type":"Researcher","foaf:name":[{"@value":"HANCHUAN PENG"}],"jpcoar:affiliationName":[{"@value":"Life Sciences/Genomics Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, 94720, USA"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"02197200"},{"@type":"EISSN","@value":"17576334"}],"prism:publicationName":[{"@value":"Journal of Bioinformatics and Computational Biology"}],"dc:publisher":[{"@value":"World Scientific Pub Co Pte Lt"}],"prism:publicationDate":"2005-04","prism:volume":"03","prism:number":"02","prism:startingPage":"185","prism:endingPage":"205"},"reviewed":"false","url":[{"@id":"https://www.worldscientific.com/doi/pdf/10.1142/S0219720005001004"}],"createdAt":"2005-04-15","modifiedAt":"2019-08-07","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050294045370835200","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Fusing sequential minimal optimization and Newton’s method for support vector training"}]},{"@id":"https://cir.nii.ac.jp/crid/1360005521379575424","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Mr<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" id=\"M1\"><mml:mrow><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>DNM: A Novel Mutual Information-Based Dendritic Neuron Model"}]},{"@id":"https://cir.nii.ac.jp/crid/1360022305562812288","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data"}]},{"@id":"https://cir.nii.ac.jp/crid/1360283693964929536","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies"}]},{"@id":"https://cir.nii.ac.jp/crid/1360302864777263360","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites"}]},{"@id":"https://cir.nii.ac.jp/crid/1360306904410758656","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Advances in Computational Pipelines and Workflows in Bioinformatics"}]},{"@id":"https://cir.nii.ac.jp/crid/1360576118702727424","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Robust Recovery of Jointly-Sparse Signals Using Minimax Concave Loss Function"}]},{"@id":"https://cir.nii.ac.jp/crid/1360588380587359360","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration"}]},{"@id":"https://cir.nii.ac.jp/crid/1360845539040734976","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Diagnostic value of blood gene expression signatures in active tuberculosis in Thais: a pilot study"}]},{"@id":"https://cir.nii.ac.jp/crid/1360861288767655296","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors"}]},{"@id":"https://cir.nii.ac.jp/crid/1360861704775582848","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"A fuzzy set based approach for effective feature selection"}]},{"@id":"https://cir.nii.ac.jp/crid/1361412896263472128","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"A Differential Evolution‐Oriented Pruning Neural Network Model for Bankruptcy Prediction"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282680450744448","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1142/s0219720005001004"},{"@type":"CROSSREF","@value":"10.1007/s13042-014-0265-x_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1155/2019/7362931_references_DOI_5uHQkGTnmUtHg3HKtFd8df7wEnD"},{"@type":"CROSSREF","@value":"10.1101/532192_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1186/s12859-015-0552-x_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1093/bioinformatics/btz333_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1109/tsp.2020.3044445_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1007/s13246-023-01308-6_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1016/b978-0-323-95502-7.00283-9_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1038/s41467-025-56276-0_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1038/gene.2015.4_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1007/s11604-023-01391-5_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1016/j.fss.2022.05.023_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"},{"@type":"CROSSREF","@value":"10.1155/2019/8682124_references_DOI_5uHQkGTnmUtHg3HKtFd8df7wEnD"},{"@type":"CROSSREF","@value":"10.1266/ggs.15-00017_references_DOI_3EuuB0ZYAKIGFTdHK6BrwdjUFLx"}]}