%0 Journal Article %1 duran2017pioneering %A Durán, Claudio %A Daminelli, Simone %A M., Thomas Josephine %A Haupt, V. Joachim %A Schroeder, Michael %A Cannistraci, Carlo Vittorio %D 2017 %J Briefings in Bioinformatics %K imported not_ies local-community-paradigm unsupervised link-prediction drug–target-interaction bipartite-complex-networks network-topology bio-inspired-computing %N 6 %P 1183--1202 %R 10.1093/bib/bbx041 %T Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory %U https://academic.oup.com/bib/article/19/6/1183/3769276 %V 19 %X The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

@article{duran2017pioneering, abstract = {The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.}, added-at = {2022-10-19T17:36:37.000+0200}, author = {Durán, Claudio and Daminelli, Simone and M., Thomas Josephine and Haupt, V. Joachim and Schroeder, Michael and Cannistraci, Carlo Vittorio}, biburl = {https://www.bibsonomy.org/bibtex/26ad5135848655b42291dabdcce235bc8/ies}, doi = {10.1093/bib/bbx041}, interhash = {bb3e0cc35d1f80cd4fa36045584bdd01}, intrahash = {6ad5135848655b42291dabdcce235bc8}, journal = {Briefings in Bioinformatics}, keywords = {imported not_ies local-community-paradigm unsupervised link-prediction drug–target-interaction bipartite-complex-networks network-topology bio-inspired-computing}, number = 6, pages = {1183--1202}, timestamp = {2022-10-19T17:36:37.000+0200}, title = {Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory}, url = {https://academic.oup.com/bib/article/19/6/1183/3769276}, volume = 19, year = 2017 }