Licensee Research Use of MEDLINE®/PubMed® Data:
Summary Report Sorted by Personal Name
This report is sorted alphabetically by the last name of the first personal name supplied. It lists NLM's MEDLINE/PubMed licensees who submitted information about their use of the data for research and permitted NLM to make their information available on the Web. Additional personal names associated with a project are included in applicable entries when supplied, but are not separate entries in this alphabetical report.
Click on the Project Summary for the corresponding complete report, or the Research Category title to view the summary report for that research category. General information about NLM's Web site for licensees' research projects is available.
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Elmer Bernstam
Elmer.V.Bernstam@uth.tmc.edu
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Research Category: Information Extraction or Retrieval Methods
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Research Project: MedlineQBE
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Project Summary: The goals of this project are to facilitate access to the biomedical literature by using techniques adapted from the World Wide Web. We are currently exploring citation analysis and collaborative filtering. In addition, we are exploring novel evaluation methods to compare alternative retrieval strategies.
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Robert Bilder
rbilder@mednet.ucla.edu
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Research Category: Ontologies or Classification Schema
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Research Project: Cognitive Phenotyping for Neuropsychiatric Therapeutics
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Project Summary: This project (part of NIH Roadmap Initiative) aims to develop strategies for interdisciplinary research on cognitive phenotypes. One goal is development of informatics approaches to cognitive phenotype definition.
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Hsinchun Chen
hchen@eller.arizona.edu
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Cathy Larson
cal@eller.arizona.edu |
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Research Category: Biological Knowledge Discovery
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Research Project: GeneScene (please note that the name of this project is undergoing revision and will soon
change) |
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Project Summary: GeneScene develops text mining and data mining techniques to support automated extraction and inference of biological regulatory pathways from biomedical literature and experimental data. We also provide visualization tools to facilitate related research.
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Andrew Emerson
a.emerson@cineca.it
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Research Category: Biological Knowledge Discovery
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Research Project: MedMOLE - Mining On-Line Expert on MedLine
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Project Summary: MedMOLE is a tool that improves the comprehension of microarray experimental results by grouping co- regulated genes on the basis of the informational content of MEDLINE documents.
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11 |
Guido Jenster
g.jenster@erasmusmc.nl
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Research Category: Biological Knowledge Discovery
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Research Project: Gene information extraction from MEDLINE
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Project Summary: We link genomic (microarray analyses) and proteomic data (protein expression analyses) to MEDLINE to be able to perform high-throughput and automated MEDLINE searches to extract relationships between genes and their gene products.
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14 |
Jeremy Jowett
jjowett@idi.org.au
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Research Category: Biological Knowledge Discovery
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Research Project: Data mining for functional information on positional candidate genes for diabetes and obesity
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Project Summary: To collate and present functional information about a list of candidate genes for a range of complex diseases including diabetes, obesity, depression and anxiety.
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19 |
Wen-Hsiang Lu
whlu@mail.ncku.edu.tw
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Research Category: Information Extraction or Retrieval Methods
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Research Project: MMODE: Cross-Language Medical Information Retrieval for Consumers
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Project Summary: Many consumers in non-English-speaking countries are eager to access up-to-date health information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that could provide Taiwanese consumers to overcome the language barrier.
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Henning Nielsen
hepn@novonordisk.com
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Research Category: Biological Knowledge Discovery
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Research Project:
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Project Summary: Teh Novo Group of Companies (Novo Nordisk, Novozymes, and Novo A/S)is a Danish Based Research, Pharmaceutical and Biotech Companies. Our main research focus is on the therapy areas Diabetes, Haemophilia, Growth Hormone, Hormone Replacement Therapy and Industrial Enzymes.
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Massimo Riccaboni
info@atalab.com
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Research Category: Information Extraction or Retrieval Methods
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Research Project:
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Project Summary: The main goals of this project are to set up automated or semi-automated procedures to translate affiliation information provided by PubMed into structured data, with a particular emphasis on separating and identifying geographical information, and to develop suitable approaches to analysis of un-structured texts in the bio-medical domain.
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Angus Roberts
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Research Category: Other
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Research Project: CLEF postgraduate studentship
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Project Summary: PhD studentship on the extraction of part whole relations from biomedical texts
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29 |
Steven Skiena
skiena@cs.sunysb.edu
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Research Category: Information Extraction or Retrieval Methods
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Research Project:
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Project Summary: TextMed is a search engine for medical entities: diseases, drugs, chemicals, organs and organisms.TextMed aims to identify relationships between these medical entities. TextMed uses natural language processing techniques to track medical entity references from the scientific literature, and a variety of statistical techniques to analyze the relationships between them.
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Peter Stoehr
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Research Category: Information Extraction or Retrieval Methods
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Research Project: Whatizit
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Project Summary: We focus on extraction of facts from scientific literature in molecular biology. This is mainly based but not limited to Pattern Matching and other High-Throughput methods. The group has experience in chunk parsing, natural language processing (NLP), and has applied its methods to different tasks. This includes identification of terminology, of abbreviations, of mutations and of relations between named entities, e.g. protein-protein interactions.
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Peter White
white@genome.chop.edu
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Research Category: Information Extraction or Retrieval Methods
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Research Project: Mining the bibliome: Information extraction of the biomedical literature
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Project Summary: Our goal is qualitatively better methods for automatically extracting information from the biomedical literature, relying on recent progress and new research in three areas: high-accuracy parsing, shallow semantic analysis, and integration of large volumes of diverse data.
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Jonathan Wren
Jonathan.Wren@OU.edu
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Research Category: Biological Knowledge Discovery
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Research Project:
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Project Summary: Knowledge discovery using entities found within MEDLINE-for both shared relationship analysis (e.g.to find commonalities for microarray responders) and implicit relationship analysis (e.g. to discover previously unknown/undocumented relationships)
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