Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer.

Journal: BMC bioinformatics
Published Date: May 20, 2015

Abstract

BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific cancers. Identification of molecular markers that are related to different types of cancers might lead to discovery of drugs that are patient and disease specific. This study aims to use microarray gene expression cancer data to identify biomarkers that are indicative of different types of cancers. Our aim is to provide a multi-class cancer classifier that can simultaneously differentiate between cancers and identify type-specific biomarkers, through the application of the Binary Coded Genetic Algorithm (BCGA) and a neural network based Extreme Learning Machine (ELM) algorithm.

Authors

  • Vasily Sachnev
    Department of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Republic of Korea. bassvasys@hotmail.com.
  • Saras Saraswathi
    Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Children's Hospital; currently at Sidra, Medical and Research Center, Doha, Qatar. ssundararajan@sidra.org.
  • Rashid Niaz
    Department of Medical Informatics, Sidra Medical and Research Center, Doha, Qatar. rniaz@sidra.org.
  • Andrzej Kloczkowski
  • Sundaram Suresh
    School of Computer Science, Nanyang Technological University, Nanyang, Singapore. ssundaram@ntu.edu.sg.

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