DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2013.62
Matteo Re , Universita degli Studi di Milano, Milano
Giorgio Valentini , Universita degli Studi di Milano, Milano
Drug repositioning is a challenging computational problem involving the integration of heterogeneous sources of biomolecular data and the design of label ranking algorithms able to exploit the overall topology of the underlying pharmacological network. In this context we propose a novel semi-supervised drug ranking problem: prioritizing drugs in integrated bio-chemical networks according to specific DrugBank therapeutic categories. Algorithms for drug repositioning usually perform the inference step into an inhomogeneous similarity space induced by the relationships existing between drugs and a second type of entity (e.g. disease, target, ligand set), thus making unfeasible a drug ranking within a homogeneous pharmacological space. To deal with this problem, we designed a general framework based on bipartite network projections by which homogeneous pharmacological networks can be constructed and integrated from heterogeneous and complementary sources of chemical, biomolecular and clinical information. Moreover, we present a novel algorithmic scheme based on kernelized score functions that adopts both local and global learning strategies to effectively rank drugs in the integrated pharmacological space using different network combination methods. Detailed experiments with more than $80$ DrugBank therapeutic categories involving about 1300 FDA approved drugs show the effectiveness of the proposed approach.
Computing Methodologies, Computer Applications, Life and Medical Sciences, Health, Graph labeling, Machine learning, Learning, Artificial Intelligence
M. Re and G. Valentini, "Network-based Drug Ranking and Repositioning with respect to DrugBank Therapeutic Categories," in IEEE/ACM Transactions on Computational Biology and Bioinformatics.