title: Pathway-level disease data mining through hyper-box principles.
abstract: 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.
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK.
authors.forename: Lingjian
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Informatics, School of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UK; St John's Institute of Dermatology, Division of Genetics and Molecular Medicine, King's College London School of Medicine, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK.
authors.forename: Chrysanthi
authors.initials: C
authors.lastname: Ainali
authors.affiliation: Department of Informatics, School of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UK.
authors.forename: Aristotelis
authors.initials: A
authors.lastname: Kittas
authors.affiliation: St John's Institute of Dermatology, Division of Genetics and Molecular Medicine, King's College London School of Medicine, Tower Wing, Guy's Hospital, Great Maze Pond, London SE1 9RT, UK.
authors.forename: Frank O
authors.initials: FO
authors.lastname: Nestle
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. Electronic address: l.papageorgiou@ucl.ac.uk.
authors.forename: Lazaros G
authors.initials: LG
authors.lastname: Papageorgiou
authors.affiliation: Department of Informatics, School of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UK. Electronic address: sophia.tsoka@kcl.ac.uk.
authors.forename: Sophia
authors.initials: S
authors.lastname: Tsoka
mesh_terms: D001943:Breast Neoplasms / Q000145:classification; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D011565:Psoriasis / Q000145:classification
keywords: Disease classification; Hyper-box-representation; Mathematical programming; Mixed integer optimisation; Pathway-based classification