Content-rich biological network constructed by mining PubMed abstracts

By Hao Chen, Burt M. Sharp. BMC Bioinformatics, 2004

397 citations recorded by the source corpus.

BackgroundThe integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public.ResultsWe present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses.ConclusionsChilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cdf00dc0ee3a525a5b92bb65c8cc4a053cf4b782

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

DISCOVERY AND EXPLANATION OF DRUG-DRUG INTERACTIONS VIA TEXT MINING (2011, methodology): “8,9 Previous work from our own group 10,11 has established methods for using a syntactical parser to identify and characterize drug-gene relationships.”
Extracting interactions between proteins from the literature (2008, neutral): “After that, researchers in biomedical domain contribute to the curation process by subsequent edits cbioc.eas.asu.edu Chilibot Chilibot [24] is a search software for MEDLINE literature database to rapidly identify relationships between genes, proteins, or any keywords that the user might be interest”
Biomedical text mining and its applications in cancer research (2013, neutral): “Chilibot [146,147] Chilibot searches PubMed abstracts about specific relationships between proteins, genes, or keywords.”
Frontiers of biomedical text mining: current progress (2007, background): “Increasing awareness of the importance of being able to map from strings in text to the things in the world (unique identifiers) or the concepts in ontologies to which they refer [127–129 , 116].”
The joint graphical lasso for inverse covariance estimation across multiple classes (2011, background): “Power law degree distributions are thought to mimic the structure of biological networks (Chen & Sharp 2004) and are generally harder to estimate than simpler structures (Peng et al. 2009).”
Identifying gene-disease associations using centrality on a literature mined gene-interaction network (2008, background): “It has been shown that the interaction networks constructed in either way, share similar topological properties such as being small-world and scale-free, with each other and with various nonbiological complex systems such as the WWW, the Internet, and social networks (Chen and Sharp, 2004; Hoffmann ”
PubMed and beyond: a survey of web tools for searching biomedical literature (2011, background): “In addition, AliBaba also presents co-occurrence results in a graph. iHop (55), Chilibot (56), PolySearch (57) and Semedico (58) are four representative systems that focus on mining associations between special topics (disobey selection criterion #2 which requires systems to handle general topics). ”
Corpus annotation for mining biomedical events from literature (2008, methodology): “, has been used successfully by many groups to develop NLP tools for the domain [36-39].”
Text-mining and information-retrieval services for molecular biology (2005, neutral): “net [57]”