Alternative feature mapping for heterogeneous gene data classification

Victor C. Liang, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In order to overcome the limitation on small sizes of gene datasets, many meta-classification methods which ensemble classifiers with different datasets have been developed. However, due to discrepancies of the characteristics within heterogeneous or cross-platform datasets, the number of common and significant genes is usually small. Instead of matching common genes between heterogeneous datasets, we propose a novel solution, alternative feature mapping approach (AFM), to utilize related and discriminative gene expressions while not necessarily having exact matches. Genes in the training dataset are clustered and mapped to the test dataset as gene groups. Through analyzing the correlation within gene groups between training and test datasets, related significant genes can be applied for classification. We conducted experiments consisting of 8 heterogeneous datasets with different cancer types and platforms to test the effectiveness of AFM. Our experiments show that classification performance is greatly improved using suitable significant genes selected by AFM.
Original languageEnglish
Title of host publicationProceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Pages138-145
Number of pages8
DOIs
Publication statusPublished - 18 Nov 2009
Event2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 - Taichung, Taiwan
Duration: 22 Jun 200924 Jun 2009

Conference

Conference2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Country/TerritoryTaiwan
CityTaichung
Period22/06/0924/06/09

Keywords

  • Classification
  • Feature
  • Heterogeneous
  • Mapping
  • Microarray

ASJC Scopus subject areas

  • Information Systems
  • Biomedical Engineering
  • Health Informatics

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