Literature mining for the biologist: from information retrieval to biological discovery

By Lars Juhl Jensen, Jasmin Saric, Peer Bork. Nature reviews genetics, 2006

710 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2942d2d712f1db6e6596ce6ebcdcc2818e85e0c3

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In Molecular Biology

Citations in the corpus

DrugComboRanker: drug combination discovery based on target network analysis (2014, background): “Drug combinations are then ranked from different drug functional communities, which inhibit disease signaling network modules.”
Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions (2009, background): “We and others have shown the great utility of statistical text mining to rapidly obtain functional information about genes, including protein-protein interactions, gene function annotation, and measuring gene-gene similarity [19–22].”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “Recognizing named entities and concepts, such as genes and diseases, in text is the basis for most biomedical applications of text mining [1].”
STITCH 4: integration of protein–chemical interactions with user data (2013, methodology): “In the text-mining channels, interactions were extracted from the literature using both co-occurrence text mining and Natural Language Processing (21,22).”
A review of feature selection techniques in bioinformatics (2007, background): “Text and literature mining is emerging as a promising area for data mining in biology [17, 54].”
Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature (2018, background): “To find the genes/proteins mentioned within the 25 PubMed titles/abstracts, we followed Jensen et al. [65] who divided the task into two: “ first, the recognition of words that refer to entities and second, the unique identification of the entities in question ” .”
Systems medicine: the future of medical genomics and healthcare (2009, methodology): “quality indices, text-mining tools, biological ontologies and the semantic web [115-120].”
SuperTarget and Matador: resources for exploring drug-target relationships (2007, methodology): “In parallel, potential drug-target relations were automatically extracted from Medline by searching for synonyms of drugs, proteins and Medical Subject Headings (MeSH terms) describing groups of proteins (7).”
Concept annotation in the CRAFT corpus (2012, background): “Substantial progress in biomedical natural-language processing (NLP), particularly in the tasks of information retrieval, concept recognition, and information extraction [1-5] raises the possibility of creating formal representations for the entire biomedical literature.”
LINNAEUS: A species name identification system for biomedical literature (2010, background): “A wide variety of biomedical text-mining tasks are currently being pursued (reviewed in [3,4]), such as entity recognition ( e.g. finding mentions of genes, proteins, diseases) and extraction of molecular relationships ( e.g. protein-protein interactions).”
STITCH: interaction networks of chemicals and proteins (2007, methodology): “Text mining of MEDLINE and OMIM yields additional evidence, based both on a simple co-occurrence scheme and a more complex natural language processing (NLP) approach (24,25).”
STITCH 3: zooming in on protein–chemical interactions (2011, methodology): “Fourth, interaction information that we extract from the literature through co-occurrence text mining and Natural Language Processing (20,21).”
Frontiers of biomedical text mining: current progress (2007, methodology): “We refer the interested reader to previous surveys [2, 5–15] (marked S in the bibliography) and to the abovementioned journals and conferences.”