Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates

DOI10.1016/j.jtbi.2009.04.013zbMath1402.92227OpenAlexW2023984830WikidataQ83752607 ScholiaQ83752607MaRDI QIDQ1624446

Ashish Anand, P. N. Suganthan

Publication date: 16 November 2018

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2009.04.013

zbMATH Keywords

cancer subtype classificationclass-wise selected genesensemble SVMmulticlass SVMprobability outputs SVM

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50) Genetics and epigenetics (92D10)

Related Items (2)

Some remarks on protein attribute prediction and pseudo amino acid composition ⋮ CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition

Uses Software

• R
• LIBSVM
• Cell-PLoc
• Memtype-2L
• ProtIdent
• Euk-PLoc
• Signal-CF
• Signal-3L
• EzyPred
• HIVcleave
• Euk-mPLoc
• Hum-PLoc

Cites Work

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• Predicting protein structural class based on multi-features fusion
• Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices
• Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
• An Empirical Distribution Function for Sampling with Incomplete Information
• An Analytical Method for Multiclass Molecular Cancer Classification
• Selection bias in gene extraction on the basis of microarray gene-expression data
• Gene selection for cancer classification using support vector machines

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