Inferring Hidden Knowledge from Biological Networks via Topological Ranking

Abstract

Motivation: It is a well established fact that the topology of biological networks yields insights into biological function, occurrence of diseases and drug design. This fact hinges on the many findings associating the biological relevance of network components/interactions with their position within the network structure. Unfortunately, to what extent the topology per se may lead to the extraction of novel biological knowledge has never been critically examined nor formalized. Not even a systematic “discovery” method is available.

Results: We propose the first algorithmic paradigm for the discovery of hidden knowledge from biological networks, based on compact views obtained from the rank induced by topological measures. An experimental evaluation of the paradigm has been conducted on six networks involving three different organisms (yeast, worm and human), nine outstanding topological measures and several internal and external (gold standard) validation criteria. The results show that the paradigm is effective, with some components of it to be preferred to others. We offer a first characterization of those components, showing that a distinct handful of best performing measures can be identified for each of the considered organisms.

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Mariella Bonomo
PhD Student

Phd Student

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Raffaele Giancarlo
Full professor

Professor of Algorithms and PI of the project: Research Unit Univ. Palermo

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Simona E. Rombo
Associate professor

Researcher on Bioinformatics, Network Analysis, Big Data