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BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

Nikolas Papanikolaou1, Georgios A Pavlopoulos1, Evangelos Pafilis1

  • 1Division of Basic Sciences, University of Crete, Medical School, Heraklion 71110, Greece, Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7, avenue des Hauts-Fourneaux, L-4362 Esch sur Alzette, Luxembourg, Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), PO Box 361, GR-57001 Thessalonica, Greece, Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, 70013 Heraklion, Crete, Greece and Department of Biological Sciences, Bioinformatics Research Laboratory, University of Cyprus, PO Box 20537, CY 1678, Nicosia, Cyprus.

Bioinformatics (Oxford, England)
|August 8, 2014
PubMed
Summary

BioTextQuest(+) is a web platform for biomedical literature mining, enhancing information retrieval and analysis. It aids researchers in discovering biological concepts and integrating data for experimental support.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Biomedical Informatics

Background:

  • The exponential growth of scientific literature presents challenges for researchers in information retrieval and data analysis.
  • Existing tools for mining PubMed and biological databases can be complex for inexperienced users.

Purpose of the Study:

  • To introduce BioTextQuest(+), an advanced web-based platform for interactive knowledge exploration and literature mining.
  • To bridge bioentity recognition, functional annotation, document clustering, and data integration for concept discovery.

Main Methods:

  • PubMed and OMIM querying for abstract retrieval.
  • Detection of genes, proteins, molecular functions, pathways, and biological processes.
  • Document clustering, term co-occurrence analysis, and tag cloud generation.
  • Integration with biological repositories and bioinformatics tools.

Main Results:

  • BioTextQuest(+) facilitates efficient literature mining and concept discovery.
  • The platform offers an intuitive interface with advanced parameterization for expert users.
  • Demonstrated utility in scenarios like author disambiguation, functional enrichment, and disease-gene association.

Conclusions:

  • BioTextQuest(+) streamlines the process of biomedical literature analysis and knowledge discovery.
  • The platform supports experimental research by integrating bioinformatics services and data.
  • It empowers both novice and expert users in navigating complex scientific corpora.