Literature DB >> 15360924

Distilling conceptual connections from MeSH co-occurrences.

Padmini Srinivasan1, Dimitar Hristovski.   

Abstract

Our aim is to contribute to biomedical text extraction and mining research. In this paper we present exploratory research on the MeSH terms assigned to MEDLINE citations. We analyze MeSH based co-occurrences and identify the interesting ones, i.e., those that are likely to be semantically meaningful. For each selected co-occurring pair we derive a weighted vector representation that emphasizes the verb based functional aspects of the underlying semantics. Preliminary experiments exploring the potential value of these vectors gave us very good results. The larger goal of this project is to contribute to knowledge discovery research by mining the knowledge that is latent within the biomedical literature. It is also to provide a method capable of suggesting cross-disciplinary connections via the pairs derived from all of MEDLINE.

Mesh:

Year:  2004        PMID: 15360924

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  5 in total

1.  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

2.  Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing.

Authors:  Jose A Miñarro-Giménez; Markus Kreuzthaler; Stefan Schulz
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

3.  Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study.

Authors:  Elizabeth S Chen; George Hripcsak; Hua Xu; Marianthi Markatou; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2007-10-18       Impact factor: 4.497

4.  Using noun phrases for navigating biomedical literature on Pubmed: how many updates are we losing track of?

Authors:  Devabhaktuni Srikrishna; Marc A Coram
Journal:  PLoS One       Date:  2011-09-14       Impact factor: 3.240

5.  Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation.

Authors:  Neil R Smalheiser; Gary Bonifield
Journal:  J Biomed Discov Collab       Date:  2016-04-06
  5 in total

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