Bioinformatics approaches in the study of cancer

Curr Mol Med. 2007 Feb;7(1):133-41. doi: 10.2174/156652407779940431.


A revolution is underway in the approach to studying the genetic basis of cancer. Massive amounts of data are now being generated via high-throughput techniques such as DNA microarray technology and new computational algorithms have been developed to aid in analysis. At the same time, standards-based repositories, including the Stanford Microarray Database and the Gene Expression Omnibus have been developed to store and disseminate the results of microarray experiments. Bioinformatics, the convergence of biology, information science, and computation, has played a key role in these developments. Recently developed techniques include Module Maps, SLAMS (Stepwise Linkage Analysis of Microarray Signatures), and COPA (Cancer Outlier Profile Analysis). What these techniques have in common is the application of novel algorithms to find high-level gene expression patterns across heterogeneous microarray experiments. Large-scale initiatives are underway as well. The Cancer Genome Atlas (TCGA) project is a logical extension of the Human Genome Project and is meant to produce a comprehensive atlas of genetic changes associated with cancer. The Cancer Biomedical Informatics Grid (caBIG), led by the NCI, also represents a colossal initiative involving virtually all aspects of cancer research and may help to transform the way cancer research is conducted and data are shared.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Animals
  • Biomarkers / metabolism
  • Computational Biology / methods*
  • Databases, Genetic
  • Humans
  • Neoplasms / genetics*
  • Oligonucleotide Array Sequence Analysis


  • Biomarkers