Abstract
In functional genomics, a pathway is defined as a set of genes which exhibit similar biological activities. Given a microarray expression data, the corresponding pathway information can be extracted with the use of some public databases. All member genes of a given pathway may not be equally relevant in estimating the activity of that pathway. Some genes can participate adequately in the given pathway, some may have low-associations. Existing literature has either considered all the genes wholly or discarded some genes completely in estimating the corresponding pathway-activity. Inspired by this, the current work reports about an automated approach to measure the degree of relevance of a given gene in predicting the pathway-activity. As a large search space has to be dragged, the exploration properties of particle swarm optimization are utilized in the current context. Particles of the PSO represent different scores of relevance for the member genes of different pathways. In order to deal with the relevance-score, the popular t-score which is widely used in measuring the pathway-activity is expanded in the name of weighted t-score. The proposed PSO-based weighted framework is then evaluated on three gene expression data sets. In order to show the supremacy of the proposed method, top 50% pathway markers are selected for each data set and the quality of these measures is checked after performing 10-fold cross-validation with respect to different quality measures. The results are further validated using biological significance tests.
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Dutta, P., Saha, S., Chauhan, A.B. (2018). Predicting Degree of Relevance of Pathway Markers from Gene Expression Data: A PSO Based Approach. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_1
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DOI: https://doi.org/10.1007/978-3-030-04221-9_1
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