Bioinformatics for characterization of genetic constitution in cancer patients

Satoshi Miyata, Masaru Ushijima, Masaaki Matsuura

Research output: Contribution to journalReview articlepeer-review


Recently many powerful technologies for detection of genome-related data have been developed and utilized to explore important biomarkers and genes related to treatment response. This article discusses high throughput genome-related data, including single nucleotide polymorphism (SNP), DNA microarray data for gene expression, and protein expression profile data. We also discuss bioinformatics methodologies suitable for analysis of genome-related data. SNP data are used to characterize genetic constitution in cancer patients. We describe the association study of SNP data and the haplotype-based analysis thought to be more efficient than the individual SNP-based approach. The gene and/or protein expression profile data are used for personalized diagnosis of cancer, and a useful method called AdaBoost is introduced. AdaBoost is robust against outliers, capable of handling missing values and appropriate for analysis of gene and/or protein expression data. Combining genome-related data, clinical information, and the medical and biological information, it is desirable to establish personalized medicine of high accuracy. For this purpose, the development of new bioinformatics methods and close cooperation in related fields such as medicine, biology, and computer science, are necessary.

Original languageEnglish
Pages (from-to)253-259
Number of pages7
Issue number3
Publication statusPublished - 2006 May 1
Externally publishedYes


  • Bioinformatics
  • DNA microarray
  • Personalized medicine
  • Proteomics
  • SNP

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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