GeneView: a comprehensive semantic search engine for PubMed

Abstract

Research results are primarily published in scientific literature and curation efforts cannot keep up with the rapid growth of published literature. The plethora of knowledge remains hidden in large text repositories like MEDLINE. Consequently, life scientists have to spend a great amount of time searching for specific information. The enormous ambiguity among most names of biomedical objects such as genes, chemicals and diseases often produces too large and unspecific search results. We present GeneView, a semantic search engine for biomedical knowledge. GeneView is built upon a comprehensively annotated version of PubMed abstracts and openly available PubMed Central full texts. This semi-structured representation of biomedical texts enables a number of features extending classical search engines. For instance, users may search for entities using unique database identifiers or they may rank documents by the number of specific mentions they contain. Annotation is performed by a multitude of state-of-the-art text-mining tools for recognizing mentions from 10 entity classes and for identifying protein-protein interactions. GeneView currently contains annotations for >194 million entities from 10 classes for ∼21 million citations with 271,000 full text bodies. GeneView can be searched at http://bc3.informatik.hu-berlin.de/.

Figure 1.

Query result for articles mentioning genes UBE2I (GeneID 7329) and BRCA1 (GeneID 672).

Figure 2.

Visualization of a selected article (PMID 21344391). Additional information such as full gene name and links to external databases can be provided for a selected entity.

Figure 3.

Number of citations where the genes BRCA1 or BRCA2 occur in for the last 20 years. Similarly, we show the progression of articles with SNPs co-mentioned with BRCA1 or BRCA2.