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Pathway-level disease data mining through hyper-box principles

Research output: Contribution to journalArticle

Lingjian Yang, Chrysanthi Ainali, Aristotelis Kittas, Frank Nestle, Lazaros G. Papageorgiou, Sophia Tsoka

Original languageEnglish
Pages (from-to)25-34
Number of pages10
JournalMathematical Biosciences
Publication statusPublished - Feb 2015

King's Authors


In microarray data analysis, traditional methods that focus on single genes are increasingly replaced by methods that analyse functional units corresponding to biochemical pathways, as these are considered to offer more insight into gene expression and disease associations. However, the development of robust pipelines to relate genotypic functional modules to disease phenotypes through known molecular interactions is still at its early stages.In this article we first discuss methodologies that employ groups of genes in disease classification tasks that aim to link gene expression patterns with disease outcome. Then we present a pathway-based approach for disease classification through a mathematical programming model based on hyper-box principles. Association rules derived from the model are extracted and discussed with respect to pathway-specific molecular patterns related to the disease. Overall, we argue that the use of gene sets corresponding to disease-relevant pathways is a promising route to uncover expression-to-phenotype relations in disease classification and we illustrate the potential of hyper-box classification in assessing the predictive power of functional pathways and uncover the effect of specific genes in the prediction of disease phenotypes.

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