title: Using ILP to Identify Pathway Activation Patterns in Systems Biology.
abstract: We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist.
authors.affiliation: Department of Informatics King's College London, Strand, London, UK.
authors.forename: Samuel R
authors.initials: SR
authors.lastname: Neaves
authors.affiliation: MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, University of Bristol, Bristol, UK; Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, UK.
authors.forename: Louise A C
authors.initials: LA
authors.lastname: Millard
authors.affiliation: Department of Informatics King's College London, Strand, London, UK.
authors.forename: Sophia
authors.initials: S
authors.lastname: Tsoka
keywords: Barcode; Biological pathways; Logical aggregation; Reactome; TreeLiker; Warmr