Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)1183-1202
Seitenumfang20
FachzeitschriftBriefings in bioinformatics
Jahrgang19
Ausgabenummer6
PublikationsstatusVeröffentlicht - 30 Mai 2017
Peer-Review-StatusJa

Externe IDs

PubMed 28453640
ORCID /0000-0003-2848-6949/work/141543413

Schlagworte

Schlagwörter

  • Bio-inspired computing, Bipartite complex networks, Drug-target interaction, Local-community-paradigm theory, Network topology, Unsupervised link prediction