A literature network of human genes for high-throughput analysis of gene expression

By T. Jenssen, A. Lægreid, J. Komorowski, Eivind Hovig. Nature Genetics, 2001

901 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/608d05ed82782a37dde41e97d7da6a9fa4bb53f2

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Citations in the corpus

Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks (2008, neutral): “Some gene databases, such as GeneCards (19) and PubGene (20), have recently included gene–chemical”
Implementing the iHOP concept for navigation of biomedical literature (2005, methodology): “…element of information extraction in medicine and biology (Fukuda et al., 1998; Proux et al., 1998; Tanabe et al., 1999; Pruitt et al., 2000; Friedman et al., 2001; Jenssen et al., 2001; Masys et al., 2001; Ono et al., 2001; Perez-Iratxeta et al., 2002; Stapley et al., 2002; Stuart et al., 2003).”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “Almost all co-occurrence methods implement a frequency-based scoring scheme to account for the fact that a pair of entities or concepts may co-occur a few times without being in any way related [19,21,22].”
ProMiner: rule-based protein and gene entity recognition (2005, methodology): “[2] composed a dictionary of gene names and used simple string matching for name detection to annotate microarray data.”
FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes (2004, background): “Direct use of biological information extracted from biomedical literature (Oliveros et al., 2000; Jenssen et al., 2000; Raychaudhuri et al., 2002) for studying such properties still supposes serious drawbacks (Blaschke et al., 2002).”
GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies (2004, methodology): “However, these methods depend on the identification of gene-refer-ence relationships and have problems such as ambiguous gene names and symbols, context of categories etc. [3].”
Extracting interactions between proteins from the literature (2008, positive): “[84] used a similar approach to find relations between human gene clusters obtained from DNA array experiments.”
Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature (2004, methodology): “Attempts to annotate gene function automatically include statistical approaches, such as cooccurrence of biological entities with a keyword or Medical Subject Heading term (Stapley and Benoit 2000; Jenssen et al. 2001).”
WebGestalt: an integrated system for exploring gene sets in various biological contexts (2005, background): “(30) have constructed a gene network from the co-occurrence of gene symbols or short gene names in the title or the abstract of a common article record.”
Biomedical text mining and its applications in cancer research (2013, neutral): “PubGene [158,159] The retrieve names of gene and protein by PubGene are cross-referenced to each other and to relevant terms with goal of understanding”
Content-rich biological network constructed by mining PubMed abstracts (2004, background): “[37-40]] have been developed to translate acronyms unambiguously into their full length terminology, since acronyms may have multiple meanings and become a source of false positives [3,41].”
Text-mining and information-retrieval services for molecular biology (2005, methodology): “PubGene [http://www.pubgene.org] 74.”
From patterns to pathways: gene expression data analysis comes of age (2002, background): “Thus, systems to augment expression analysis with automated literature extraction or organization 94–96 are likely to prove extremely valuable in drawing meaningful and reproducible conclusions.”