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Start Over Author "Josephine Maria Thomas" Topic drug interactions
2 results on '"Josephine Maria Thomas"'

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

Author

V. Joachim Haupt, Josephine Maria Thomas, Claudio Durán, Carlo Vittorio Cannistraci, Michael Schroeder, and Simone Daminelli

Subjects
0301 basic medicine, Paper, bio-inspired computing, Exploit, Computer science, bipartite complex networks, Machine learning, computer.software_genre, Topology, network topology, local-community-paradigm theory, 03 medical and health sciences, Drug Delivery Systems, Drug Discovery, Drug Interactions, unsupervised link prediction, Representation (mathematics), Molecular Biology, Topology (chemistry), Self-organization, Class (computer programming), business.industry, drug–target interaction, Intelligent decision support system, Brain, Computational Biology, Reproducibility of Results, 030104 developmental biology, Bipartite graph, Artificial intelligence, business, computer, Biological network, Algorithms, Information Systems
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.

Published
2017

2. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks.

Author

Simone Daminelli, Josephine Maria Thomas, Claudio Durán, and Carlo Vittorio Cannistraci

Subjects
*BIPARTITE graphs, *DRUG interactions, *STOCHASTIC models
Abstract

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain. [ABSTRACT FROM AUTHOR]

Published
2015
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