Text-mining and information-retrieval services for molecular biology
By Martin Krallinger, Alfonso Valencia. 2005
236 citations recorded by the source corpus.
Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.
Subfields: Artificial Intelligence, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dina Demner-Fushman
- Hong Yu
- Pierre Zweigenbaum
- Ryan M. Kelley
- Hsueh‐Fen Juan
- Christopher Sander
- Gary D. Bader
- Leroy Hood
In Molecular Biology
- Trey Ideker
- Gary D. Bader
- Alexander R. Pico
- J. H. Morris
- Leroy Hood
- Christopher Sander
- Kristina Hanspers
- Bruce R. Conklin
Citations in the corpus
- Predicting protein function from sequence and structure (2007, background): “Although natural language processing and automatic extraction of information from biological literature continues to improv”
- Frontiers of biomedical text mining: current progress (2007, background): “mining applications (see [9, 12] for reviews of this early work).”
- Integration of biological networks and gene expression data using Cytoscape (2007, background): “When information is not available in databases but is in the literature, text-mining techniques can extract functional relationships between recognized genes that, while not always accurate, are useful for analysis in aggregat”
- 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].”
- MeInfoText: associated gene methylation and cancer information from text mining (2008, background): “Text mining in biology is to automatically extract specific information about genes, proteins and their functional associations from text documents [11].”