The pathway-based microarray classification approach leads to a new era of genomic research. However, this approach is limited by the issues in quality of pathway data. Usually the pathway data are curated from biological literatures and in specific biological experiment (e.g., lung cancer experiment), context free pathway information collection process takes place leading to the presence of uninformative genes in the pathways. Many methods in this approach neglect these limitations by treating all genes in a pathway as significant. In this paper, we proposed a hybrid of support vector machine and smoothly clipped absolute deviation with group-specific tuning parameters (gSVM-SCAD) to select informative genes within pathways before the pathway evaluation process. Our experiment on canine, gender and lung cancer datasets shows that gSVM-SCAD obtains significant results in identifying significant genes and pathways and in classification accuracy.

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In this reiew, we have discussed about approaches in pathway based microarray analysis. Commonly, there are two approaches in pathway based analysis, Enrichment Score and Supervised Machine Learning. These pathway based approaches usually aim to statistically define significant pathways that related to phenotypes of interest. Firstly we discussed an overview of pathway based microarray analysis and its general flow processes in scoring the pathways, the methods applied in both approaches, advantages and limitations based on current researches, and pathways database used in pathway analysis. This review aim to provide better understanding about pathway based microarray analysis and its approaches.

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