Combining Support Vector Machines and the t-statistic for Gene Selection in DNA Microarray Data Analysis

No abstract is available for this paper.

• Publication year

  2010

• Venue

  Pacific-Asia Conference on Knowledge Discovery and Data Mining

• Publication date

  2010-06-21

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.1007/978-3-642-13672-6_6
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning

  2006cited by this paper
• Simple decision rules for classifying human cancers from gene expression profiles

  2005cited by this paper
• Gene extraction for cancer diagnosis by support vector machines - An improvement

  2005influential reference
• An extensive comparison of recent classification tools applied to microarray data

  2004influential reference
• Linear regression and two-class classification with gene expression data

  2003cited by this paper
• Gene Selection for Cancer Classification using Support Vector Machines

  2002influential reference
• Selection bias in gene extraction on the basis of microarray gene-expression data

  2002influential reference
• Support vector machine classification and validation of cancer tissue samples using microarray expression data

  2000cited by this paper

• Graph coloring for extracting discriminative genes in cancer data

  2019cites this paper
• t-INDEPENDENT COMPONENT ANALYSIS FOR SVM CLASSIFICATION OF DNA-MICROARRAY DATA

  2015cites this paper
• Interaction-Based Learning for High-Dimensional Data with Continuous Predictors

  2014cites this paper
• An Ensemble-Based Feature Selection Algorithm Using Combination of Support Vector Machine and Filter Methods for Solving Classification Problems

  2013cites this paper
• Logic minimization and rule extraction for identification of functional sites in molecular sequences

  2012cites this paper