Literature DB >> 15262811

Mining MEDLINE for implicit links between dietary substances and diseases.

Padmini Srinivasan1, Bisharah Libbus.   

Abstract

MOTIVATION: Text mining systems aim at knowledge discovery from text collections. This work presents our text mining algorithm and demonstrates its use to uncover information that could form the basis of new hypotheses. In particular, we use it to discover novel uses for Curcuma longa, a dietary substance, which is highly regarded for its therapeutic properties in Asia.
RESULTS: Several disease were identified that offer novel research contexts for curcumin. We analyze select suggestions, such as retinal diseases, Crohn's disease and disorders related to the spinal cord. Our analysis suggests that there is strong evidence in favor of a beneficial role for curcumin in these diseases. The evidence is based on curcumin's influence on several genes, such as COX-2, TNF-alpha, JNK, p38 MAPK and TGF-beta. This research suggests that our discovery algorithm may be used to suggest novel uses for dietary and pharmacological substances. More generally, our text mining algorithm may be used to uncover information that potentially sheds new light on a given topic of interest. AVAILABILITY: Contact authors.

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Year:  2004        PMID: 15262811     DOI: 10.1093/bioinformatics/bth914

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  32 in total

1.  Mining connections between chemicals, proteins, and diseases extracted from Medline annotations.

Authors:  Nancy C Baker; Bradley M Hemminger
Journal:  J Biomed Inform       Date:  2010-03-27       Impact factor: 6.317

2.  Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

Authors:  A M Cohen; W R Hersh; C Dubay; K Spackman
Journal:  BMC Bioinformatics       Date:  2005-04-22       Impact factor: 3.169

3.  Exploiting semantic relations for literature-based discovery.

Authors:  Dimitar Hristovski; Carol Friedman; Thomas C Rindflesch; Borut Peterlin
Journal:  AMIA Annu Symp Proc       Date:  2006

4.  Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction.

Authors:  Aurélie Névéol; Rezarta Islamaj Doğan; Zhiyong Lu
Journal:  J Biomed Inform       Date:  2010-11-20       Impact factor: 6.317

5.  Using the literature-based discovery paradigm to investigate drug mechanisms.

Authors:  Caroline B Ahlers; Dimitar Hristovski; Halil Kilicoglu; Thomas C Rindflesch
Journal:  AMIA Annu Symp Proc       Date:  2007-10-11

6.  Finding potentially new multimorbidity patterns of psychiatric and somatic diseases: exploring the use of literature-based discovery in primary care research.

Authors:  Rein Vos; Sil Aarts; Erik van Mulligen; Job Metsemakers; Martin P van Boxtel; Frans Verhey; Marjan van den Akker
Journal:  J Am Med Inform Assoc       Date:  2013-06-17       Impact factor: 4.497

Review 7.  Recent progress in automatically extracting information from the pharmacogenomic literature.

Authors:  Yael Garten; Adrien Coulet; Russ B Altman
Journal:  Pharmacogenomics       Date:  2010-10       Impact factor: 2.533

8.  PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.

Authors:  Yucan Zhang; Indra Neil Sarkar; Elizabeth S Chen
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

9.  Mining the pharmacogenomics literature--a survey of the state of the art.

Authors:  Udo Hahn; K Bretonnel Cohen; Yael Garten; Nigam H Shah
Journal:  Brief Bioinform       Date:  2012-07       Impact factor: 11.622

10.  Annotating the human genome with Disease Ontology.

Authors:  John D Osborne; Jared Flatow; Michelle Holko; Simon M Lin; Warren A Kibbe; Lihua Julie Zhu; Maria I Danila; Gang Feng; Rex L Chisholm
Journal:  BMC Genomics       Date:  2009-07-07       Impact factor: 3.969

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