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Volume 3 Supplement 3

Machine Learning for Biomedical Literature Analysis and Text Retrieval in the International Conference on Machine Learning and Applications 2011

Research

Edited by Rezarta Islamaj Dogan and Lana Yeganova

Machine Learning for Biomedical Literature Analysis and Text Retrieval in the International Conference on Machine Learning and Applications 2011.

Honolulu, HI, USA18-21 December 2011

  1. Content type: Research

    We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempt...

    Authors: KE Ravikumar, Haibin Liu, Judith D Cohn, Michael E Wall and Karin Verspoor

    Citation: Journal of Biomedical Semantics 2012 3(Suppl 3):S2

    Published on:

  2. Content type: Research

    There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly define...

    Authors: Lana Yeganova, Won Kim, Donald C Comeau and W John Wilbur

    Citation: Journal of Biomedical Semantics 2012 3(Suppl 3):S3

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  3. Content type: Research

    Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genr...

    Authors: Rohit J Kate

    Citation: Journal of Biomedical Semantics 2012 3(Suppl 3):S4

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  4. Content type: Research

    Clustering textual contents is an important step in mining useful information on the web or other text-based resources. The common task in text clustering is to handle text in a multi-dimensional space, and to...

    Authors: Sun Kim and W John Wilbur

    Citation: Journal of Biomedical Semantics 2012 3(Suppl 3):S6

    Published on:

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