by Lauri Ma Eronen, Hannu Tt Toivonen
Abstract:
ABSTRACT: BACKGROUND: Biological databases contain large amounts of data concerning the functions and associationsof genes and proteins. Integration of data from several such databases into a single repositorycan aid the discovery of previously unknown connections spanning multiple types ofrelationships and databases. RESULTS: Biomine is a system that integrates cross-references from several biological databases into agraph model with multiple types of edges, such as protein interactions, gene-diseaseassociations and gene ontology annotations. Edges are weighted based on their type,reliability, and informativeness. We present Biomine and evaluate its performance in linkprediction, 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 diseasegene prioritization tasks as instances of link prediction. The predictions are based on aproximity measure computed on the integrated graph. We consider and experiment withseveral such measures, and perform a parameter optimization procedure where different edgetypes are weighted to optimize link prediction accuracy. We also propose a novel method fordisease-gene prioritization, defined as finding a subset of candidate genes that cluster togetherin the graph. We experimentally evaluate Biomine by predicting future annotations in thesource databases and prioritizing lists of putative disease genes. CONCLUSIONS: The experimental results show that Biomine has strong potential for predicting links when aset of selected candidate links is available. The predictions obtained using the entire Biominedataset 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, anestablished reference set of disease-associated genes is useful, but the results show that underfavorable 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 and8.1 million relations between them, with focus on human genetics. Some of its functionalitiesare available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching forand visualizing connections between given biological entities.
Reference:
Biomine: predicting links between biological entities using network models of heterogeneous databases. (Lauri Ma Eronen, Hannu Tt Toivonen), In BMC bioinformatics, volume 13, 2012.
Bibtex Entry:
@article{Eronen2012,
abstract = {ABSTRACT: BACKGROUND: Biological databases contain large amounts of data concerning the functions and associationsof genes and proteins. Integration of data from several such databases into a single repositorycan aid the discovery of previously unknown connections spanning multiple types ofrelationships and databases. RESULTS: Biomine is a system that integrates cross-references from several biological databases into agraph model with multiple types of edges, such as protein interactions, gene-diseaseassociations and gene ontology annotations. Edges are weighted based on their type,reliability, and informativeness. We present Biomine and evaluate its performance in linkprediction, 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 diseasegene prioritization tasks as instances of link prediction. The predictions are based on aproximity measure computed on the integrated graph. We consider and experiment withseveral such measures, and perform a parameter optimization procedure where different edgetypes are weighted to optimize link prediction accuracy. We also propose a novel method fordisease-gene prioritization, defined as finding a subset of candidate genes that cluster togetherin the graph. We experimentally evaluate Biomine by predicting future annotations in thesource databases and prioritizing lists of putative disease genes. CONCLUSIONS: The experimental results show that Biomine has strong potential for predicting links when aset of selected candidate links is available. The predictions obtained using the entire Biominedataset 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, anestablished reference set of disease-associated genes is useful, but the results show that underfavorable 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 and8.1 million relations between them, with focus on human genetics. Some of its functionalitiesare available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching forand visualizing connections between given biological entities.},
author = {Eronen, Lauri Ma and Toivonen, Hannu Tt},
doi = {10.1186/1471-2105-13-119},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = jun,
number = {1},
pages = {119},
pmid = {22672646},
title = {{Biomine: predicting links between biological entities using network models of heterogeneous databases.}},
url = {http://www.biomedcentral.com/1471-2105/13/119},
volume = {13},
year = {2012}
}