ECCB 2014 Accepted Posters with Abstracts D: Computational systems biology

PATHiPRED: prediction models using the activation status of stimulus-response signaling circuits. Abstract: Signaling pathways provide a formal representation of the processes by which the cell triggers actions in response to particular stimulus through a network of intermediate gene products. Consequently, sub-networks corresponding to stimulus-response circuits can directly be related to cell functionalities. Some recent pathway topology based methods focus particularly on the estimation of the activity of stimulus-response signaling circuits from gene expression data [1,2]. Here, we present PATHiPRED web tool that takes advantage of changes detected in the activity of stimulus-response signaling circuits for predictive purposes. The activation status of elementary components of signaling pathways are rich-informative biomarkers that can be linked to specific cell functionalities and provide mechanistic explanations for the molecular basis of complex traits. A major challenge in the diagnosis and treatment of complex diseases is to identify relevant alterations in the biological pathway activities and discover their relationship to the disease. Using stimulus-response signaling circuits to distinguish between two classes, such as control and disease samples, or continuous variables, such as drug activity, would improve our understanding of complex phenotypes. PATHiPRED uses the probability of activation of stimulus-response signaling circuits obtained with PATHiWAYS methodology [2] to compute a pathway-based classifier for either two classes or continuous variables. Prediction models are obtained with a method based on SVM with cross-validation and it can be used to predict new datasets within the web applicatio