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Eric P. Hoffman

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JMLR Journal 2013 Journal Article

The CAM Software for Nonnegative Blind Source Separation in R-Java

  • Niya Wang
  • Fan Meng
  • Li Chen
  • Subha Madhavan
  • Robert Clarke
  • Eric P. Hoffman
  • Jianhua Xuan
  • Yue Wang

We describe a R-Java CAM (convex analysis of mixtures) package that provides comprehensive analytic functions and a graphic user interface ( GUI ) for blindly separating mixed nonnegative sources. This open-source multiplatform software implements recent and classic algorithms in the literature including Chan et al. (2008), Wang et al. (2010), Chen et al. (2011a) and Chen et al. (2011b). The CAM package offers several attractive features: (1) instead of using proprietary MATLAB, its analytic functions are written in R, which makes the codes more portable and easier to modify; (2) besides producing and plotting results in R, it also provides a Java GUI for automatic progress update and convenient visual monitoring; (3) multi-thread interactions between the R and Java modules are driven and integrated by a Java GUI, assuring that the whole CAM software runs responsively; (4) the package offers a simple mechanism to allow others to plug-in additional R -functions. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2013. ( edit, beta )

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JMLR Journal 2010 Journal Article

Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases

  • Guoqiang Yu
  • Yuanjian Feng
  • David J. Miller
  • Jianhua Xuan
  • Eric P. Hoffman
  • Robert Clarke
  • Ben Davidson
  • Ie-Ming Shih

Microarray gene expressions provide new opportunities for molecular classification of heterogeneous diseases. Although various reported classification schemes show impressive performance, most existing gene selection methods are suboptimal and are not well-matched to the unique characteristics of the multicategory classification problem. Matched design of the gene selection method and a committee classifier is needed for identifying a small set of gene markers that achieve accurate multicategory classification while being both statistically reproducible and biologically plausible. We report a simpler and yet more accurate strategy than previous works for multicategory classification of heterogeneous diseases. Our method selects the union of one-versus-everyone (OVE) phenotypic up-regulated genes (PUGs) and matches this gene selection with a one-versus-rest support vector machine (OVRSVM). Our approach provides even-handed gene resources for discriminating both neighboring and well-separated classes. Consistent with the OVRSVM structure, we evaluated the fold changes of OVE gene expressions and found that only a small number of high-ranked genes were required to achieve superior accuracy for multicategory classification. We tested the proposed PUG-OVRSVM method on six real microarray gene expression data sets (five public benchmarks and one in-house data set) and two simulation data sets, observing significantly improved performance with lower error rates, fewer marker genes, and higher performance sustainability, as compared to several widely-adopted gene selection and classification methods. The MATLAB toolbox, experiment data and supplement files are available at http://www.cbil.ece.vt.edu/software.htm. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

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