Fostering Biological Relevance in Feature Selection for Microarray Data

IEEE Intelligent Systems

Volumn 20, Issue 6, 2005, Pages 71-73

  Berens, Michael ^a   Liu, Huan ^b   Parsons, Lance ^b   Zhao, Zheng ^b   Yu, Lei ^c  

^a TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c BINGHAMTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICALLY ANALYSIS; FEATURE SELECTION;

BIOLOGY; GENES; STATISTICAL METHODS;

DATA REDUCTION;

EID: 30344487234     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (5)

1. G. Piatetsky-Shapiro and P. Tamayo, "Microarray Data Mining: Facing the Challenges," SIGKDD Explorations Newsletter, vol. 5, no. 2, 2003, pp. 1-5.
2. H. Liu and L. Yu, "Toward Integrating Feature Selection Algorithms for Classification and Clustering," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 3, 2005, pp. 1-12.
3. J.L. Rennert et al., "Supervised Pattern Recognition Identifies Principal Components of Differential Gene Expression Related to Brain Tumor Migration," Proc. Oncogenomics 2005: Dissecting Cancer Through Genome Research, Am. Assoc. for Cancer Research, 2005, p. B38.
4. L. Yu et al., Exploiting Statistical Redundancy in Expression Microarray Data to Foster Biological Relevancy, tech. report TR-05-005, Computer Science and Eng. Dept., Arizona State Univ., 2005.
5. L. Yu and H. Liu, "Redundancy Based Feature Selection for Microarray Data," Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 04), 2004, pp. 737-742.