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Open Access Highly Accessed Methodology article

Biomine: predicting links between biological entities using network models of heterogeneous databases

Lauri Eronen12* and Hannu Toivonen2

Author affiliations

1 Biocomputing Platforms Ltd, Innopoli 2, Tekniikantie 14, , FI-02150 Espoo, Finland

2 Department of Computer Science and HIIT, University of Helsinki, PO Box 68, Helsinki, FI–00014, Finland

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Citation and License

BMC Bioinformatics 2012, 13:119  doi:10.1186/1471-2105-13-119

Published: 6 June 2012

Abstract

Background

Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases.

Results

Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes.

Conclusions

The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.

The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi webcite, allowing searching for and visualizing connections between given biological entities.