Exception discovery: A novel method for the identification of differentially expressed proteins

Jung Eun Shim, Dan Bi Kim, Won Suk Lee, Sang Yun Cho, Eunsil Yu

Research output: Contribution to journalArticlepeer-review


The identification of differentially expressed proteins (DEPs) observed under specific conditions is one of the key issues in proteomics research. There are currently several ways to detect the changes of a specific proteins expression level in two-dimensional electrophoresis (2-DE) gel images such as statistical analysis and graphical visualization. However, it is quite difficult to handle the information of an individual protein manually by these methods due to the large distortions of patterns in 2-DE images. This paper proposes a method of analyzing DEPs for a specific disease. In order to automatically extract meaningful DEPs in a set of 2-DE gel images, we have designed an exception function that is suitable to measure the anomalous change of the expression level of an individual protein. We present the comparison results of the proposed method versus a Wilcoxon paired t-test that is one of the widely used statistical analysis methods. Several experiments are performed to address not only the effectiveness of the exception function but also the fact that these two methods can compensate each other practically.

Original languageEnglish
Article number4446719
Pages (from-to)473-480
Number of pages8
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number2
Publication statusPublished - 2010 Mar

Bibliographical note

Funding Information:
Manuscript received May 27, 2007; revised October 9, 2007. First published February 2, 2008; current version published March 17, 2010. This work was supported in part by the Korea Science and Engineering Foundation (KOSEF) under the National Research Laboratory Program funded by the Ministry of Science and Technology (R0A-2006-000-10225-0) and in part by the KOSEF grant funded by the Korea Government (MOST) (R01-2006-000-11223-0).

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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