Identifying gene-disease associations using centrality on a literature mined gene-interaction network

By Arzucan Özgür, Thuy Duong Vu, Günes Erkan, Dragomir R. Radev. Intelligent Systems in Molecular Biology, 2008

386 citations recorded by the source corpus.

Motivation: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. Results: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. Availability: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org Contact: radev@umich.edu

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1867d50435eff8a68e917d13c0d19c517974e0d7

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In Molecular Biology

Citations in the corpus

Mining breast cancer genes with a network based noise-tolerant approach (2013, background): “Several papers had shown that cancer genes could be effectively distinguished from others by their topological attributes in the PPI network, such as node degree [47], betweenness centrality [48] and shortest path length [10].”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, methodology): “To keep up with the exponential growth of the literature, automated methods have been applied to mining protein-protein interactions [15,16], gene-phenotype associations [17,18], gene ontology [19], and pathway information [20], which we collectively call biomolecular information extraction.”
A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature (2010, background): “. Such tools are not only used to populate PPI databases, but their output is often also used directly as independent input to biological data mining (see, e.g.,”
The power of protein interaction networks for associating genes with diseases (2010, neutral): “A similar idea was recently used by Radivojac et al. (2008) as one of many integrative features in an support vector machine (SVM) to predict disease annotations, and by Lavallee-Adam et al.”
An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, background): “…words are verbs and nouns, and because the dependency grammar (DG) views the verb as the structural center of all clause structure, dependency grammar is very fit for relation extraction, and a lot of previous studies extract biomedical relations are based on dependency parsing [7] [20–21].”
Network analyses in systems pharmacology (2009, background): “Similarly, interacting proteins can be involved in a disease process (Goh et al., 2007; Ozgur et al., 2008).”
Using graph theory to analyze biological networks (2011, background): “The paths from the starting to the ending node are among others, to identify synthetic genetic interactions [85], gene-disease associations [86] or network hubs [77].”