Belief combination for uncertainty reduction in microarray gene expression pattern analysis

Kajia Cao, Qiuming Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Many classification methods are used in microarray gene expression data analysis to identify genes that are predictive to clinical outcomes (survival/fatal) of certain diseases. However, the reliability of these methods is often not well established due to the imprecision of the method and uncertainty of the dataset. In this paper, a knowledge-based belief reasoning system (BRS) is proposed to solve the problem by dealing with the uncertainties inherent in the results of various classification methods. Through the belief combination process, we pursue a means to reduce the uncertainty and improve the reliability of classification so that the underlying features of gene behavior recorded in the microarray expression profiles could be convincingly revealed.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
Pages844-851
Number of pages8
EditionPART 3
StatePublished - Dec 1 2007
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: May 27 2007May 30 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume4489 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Science, ICCS 2007
CountryChina
CityBeijing
Period5/27/075/30/07

Fingerprint

Gene Expression Analysis
Pattern Analysis
Gene Expression Profiling
Microarrays
Gene expression
Microarray
Uncertainty
Genes
Gene
Fatal Outcome
Imprecision
Knowledge-based
Gene Expression Data
Microarray Data
Data analysis
Reasoning
Gene Expression
Beliefs

Keywords

  • Belief reasoning system
  • Dempster shafer theory
  • Microarray gene expression
  • Uncertainty reasoning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cao, K., & Zhu, Q. (2007). Belief combination for uncertainty reduction in microarray gene expression pattern analysis. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings (PART 3 ed., pp. 844-851). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

Belief combination for uncertainty reduction in microarray gene expression pattern analysis. / Cao, Kajia; Zhu, Qiuming.

Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. 2007. p. 844-851 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Cao, K & Zhu, Q 2007, Belief combination for uncertainty reduction in microarray gene expression pattern analysis. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4489 LNCS, pp. 844-851, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 5/27/07.
Cao K, Zhu Q. Belief combination for uncertainty reduction in microarray gene expression pattern analysis. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 ed. 2007. p. 844-851. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
Cao, Kajia ; Zhu, Qiuming. / Belief combination for uncertainty reduction in microarray gene expression pattern analysis. Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. 2007. pp. 844-851 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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