Combination methods in microarray analysis

Han Yu Chuang, Hongfang Liu, Fang An Chen, Cheng Yan Kao, D. Frank Hsu

Research output: Contribution to conferencePaper

10 Scopus citations

Abstract

Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurement in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combinationi sefficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.

Original languageEnglish (US)
Pages625-630
Number of pages6
StatePublished - Aug 16 2004
EventProceedings on the International Symposium on Parallel Architectures, Algorithms and Networks, I-SPAN - Hong Kong, China
Duration: May 10 2004May 12 2004

Other

OtherProceedings on the International Symposium on Parallel Architectures, Algorithms and Networks, I-SPAN
CountryChina
CityHong Kong
Period5/10/045/12/04

ASJC Scopus subject areas

  • Computer Science(all)

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    Chuang, H. Y., Liu, H., Chen, F. A., Kao, C. Y., & Hsu, D. F. (2004). Combination methods in microarray analysis. 625-630. Paper presented at Proceedings on the International Symposium on Parallel Architectures, Algorithms and Networks, I-SPAN, Hong Kong, China.