Data Intensive Computing: A Biomedical Case Study in Gene Selection and Filtering

In data intensive computing environments where the number of samples and data dimensions grow sufficiently large, existing methods in Bioinformatics research are not effective for selecting important genes. In this chapter, we propose two approaches for parallel selection of genes, both are based on the well known { ReliefF} feature selection method and cluster computing environments. In the first design, denoted by { PReliefF} p , the input data are split into non-overlapping subsets assigned to cluster nodes. Each node carries out gene selection by using the { ReliefF} method on its own subset, without interaction with other clusters. The final ranking of the genes for selection is generated by gathering weight vectors from all nodes. In the second design, namely { PReliefF} g , each node dynamically updates global weight vectors so the gene selection results in one node can be used to boost the selection process for other nodes. Experimental results from real-world microarray expression data show that { PReliefF} p and { PReliefF} g nearly perfectly speedup to the number of nodes involved in the computing. When combined with several popular classification methods, the classifiers built from the genes selected from both methods have the same or even better methods than the genes selected from the original ReliefF method.