title: Finding rule groups to classify high dimensional gene expression datasets.
abstract: Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods cannot be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes) to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches.
authors.affiliation: Faculty of Science and Technology, Deakin University, Melbourne, Victoria, Australia. Jiyuan@deakin.edu.au
authors.forename: Jiyuan
authors.initials: J
authors.lastname: An
authors.forename: Yi-Ping Phoebe
authors.initials: YP
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D015870:Gene Expression; D020411:Oligonucleotide Array Sequence Analysis