Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data.

Journal: PloS one
Published Date: Jan 1, 2019

Abstract

BACKGROUND: Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-view data sets. While the kernel based methods can overcome these problems, a well-founded positive definite kernel based GS method has yet to be proposed for biomedical data analysis.

Authors

  • Md Ashad Alam
    Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA. Electronic address: malam@tulane.edu.
  • Mohammd Shahjaman
    Department of Statistics, Begum Rokeya University, Rangpur 5400, Bangladesh.
  • Md Ferdush Rahman
    Department of Marketing, Begum Rokeya University, Rangpur 5400, Bangladesh.
  • Fokhrul Hossain
    Department of Genetics, Stanley S. Scott Cancer Center, LSU Health Sciences Center, Louisiana State University, New Orleans, LA 70112, United States of America.
  • Hong-Wen Deng
    Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA 70112, USA.

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