Specific tuning parameter for directed random walk algorithm cancer classification

Choon Sen Seah, Shahreen Kasim, Mohd Saberi Mohamad

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.

Original languageEnglish
Pages (from-to)176-182
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Issue number1
Publication statusPublished - 2017
Externally publishedYes


  • Cancer classification
  • Directed random walk algorithm
  • Group specific tuning parameter

ASJC Scopus subject areas

  • General Computer Science
  • General Agricultural and Biological Sciences
  • General Engineering


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