Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

Abstract

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.

Possible Links between Sickle Cell Crisis and Pentavalent Antimony

“…Using such text-based inference, researchers have predicted new therapies for disease, novel applications of existing drugs, and connections between diseases. 5,[17][18][19][20] ChemoText was constructed by extracting the MeSH annotations from each article in MEDLINE, the database behind the National Library of Medicine's PubMed. The annotations were processed and organized into a database that allows known relationships between chemicals, proteins, and disease to be explored and new relationships to be inferred.…”

Associating disease-related genetic variants in intergenic regions to the genes they impact

“…There are literature-based methods that have addressed association of genes to disease based on co-occurrences for a set of articles defined by a user query (Rebholz-Schuhmann et al, 2007;Tsuruoka et al, 2008) or that rely on matching of MeSH profiles (Cheung et al, 2012;Xiang et al, 2013). Other methods use indirect relations from co-occurrence analysis (Frijters et al, 2010) or based on interaction networks (Gonzalez et al, 2006;Özgür et al, 2008). Some combine different sources of information including the literature, such as Endeavour (Aerts et al, 2006), DisGeNET (Bauer-Mehren et al, 2010), G2D (Perez-Iratxeta et al, 2005), MimMiner (van Driel et al, 2006), PolySearch (Cheng et al, 2008) or the approach proposed by Tiffin et al (2005).…”

A Correlation Analysis Model for Multidisciplinary Data in Disaster Research

“…Meliha and Wanda (2006) incorporated knowledge-based methodologies with a statistical method to mine the biomedical literature for new, potentially causal connections between biomedical terms. Frijters et al (2010) employed literature mining to discover hidden connections between drugs, genes, and diseases.…”