Knowledge Discovery in Life Science Literature [electronic resource] : PAKDD 2006 International Workshop, KDLL 2006, Singapore, April 9, 2006. Proceedings / edited by Eric G. Bremer, Jörg Hakenberg, Eui-Hong (Sam) Han, Daniel Berrar, Werner Dubitzky.

By: Bremer, Eric G [editor.]Contributor(s): Hakenberg, Jörg [editor.] | Han, Eui-Hong (Sam) [editor.] | Berrar, Daniel [editor.] | Dubitzky, Werner [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 3886Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: XIV, 147 p. Also available online. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540328100Subject(s): Computer science | Medical records -- Data processing | Database management | Information storage and retrieval systems | Artificial intelligence | Bioinformatics | Computer Science | Database Management | Artificial Intelligence (incl. Robotics) | Information Storage and Retrieval | Bioinformatics | Probability and Statistics in Computer Science | Health InformaticsAdditional physical formats: Printed edition:: No titleDDC classification: 005.74 LOC classification: QA76.9.D3Online resources: Click here to access online
Contents:
Alignment of Biomedical Ontologies Using Life Science Literature -- Improving Literature Preselection by Searching for Images -- Headwords and Suffixes in Biomedical Names -- A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature -- Recognizing Biomedical Named Entities Using SVMs: Improving Recognition Performance with a Minimal Set of Features -- Investigation of the Changes of Temporal Topic Profiles in Biomedical Literature -- Extracting Protein-Protein Interactions in Biomedical Literature Using an Existing Syntactic Parser -- Extracting Named Entities Using Support Vector Machines -- Extracting Initial and Reliable Negative Documents to Enhance Classification Performance -- Detecting Invalid Dictionary Entries for Biomedical Text Mining -- Automated Identification of Protein Classification and Detection of Annotation Errors in Protein Databases Using Statistical Approaches -- GetItFull – A Tool for Downloading and Pre-processing Full-Text Journal Articles.
In: Springer eBooksSummary: This volume of the Springer Lecture Notes in Computer Science series contains the contributions presented at the International Workshop on Knowledge D- coveryLifeScienceLiterature2006(KDLL2006)heldinSingapore,9April2006, in conjunction with the 10th Paci?c-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The life sciences encompass research and development in areas such as bi- ogy, pharmacology, biophysics, biochemistry, neuroscience, medicine, and en- ronmental sciences. A common theme among life science disciplines is the desire to understand the stimuli-response mechanisms of biological entities, systems, and processes at di?erent levels of organization—from molecules to organisms to ecosystems. Asnaturalphenomenaarebeingprobedandmappedinever-greater detail, life scientists are generating an increasingly growing amount of textual information in the form of full-text research articles, abstracts, Web content, - ports, books, and so on. Even in well-focused subject areas it is becoming more and more di?cult for researchers and practitioners to ?nd, read, and process all textual information relevant to their tasks. Knowledge discovery in text (KDT) is a fast-developing ?eld that encompasses a variety of methodologies, me- ods and tools, which facilitate automated processing of text information stored in electronic format. KDT tasks that are particularly interesting to life science include: • Identi?cation and retrieval of relevant documents from one or more large collections of documents; • Identi?cation of relevant sections in large documents (passage retrieval); • Co-reference resolution, i. e.
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Alignment of Biomedical Ontologies Using Life Science Literature -- Improving Literature Preselection by Searching for Images -- Headwords and Suffixes in Biomedical Names -- A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature -- Recognizing Biomedical Named Entities Using SVMs: Improving Recognition Performance with a Minimal Set of Features -- Investigation of the Changes of Temporal Topic Profiles in Biomedical Literature -- Extracting Protein-Protein Interactions in Biomedical Literature Using an Existing Syntactic Parser -- Extracting Named Entities Using Support Vector Machines -- Extracting Initial and Reliable Negative Documents to Enhance Classification Performance -- Detecting Invalid Dictionary Entries for Biomedical Text Mining -- Automated Identification of Protein Classification and Detection of Annotation Errors in Protein Databases Using Statistical Approaches -- GetItFull – A Tool for Downloading and Pre-processing Full-Text Journal Articles.

This volume of the Springer Lecture Notes in Computer Science series contains the contributions presented at the International Workshop on Knowledge D- coveryLifeScienceLiterature2006(KDLL2006)heldinSingapore,9April2006, in conjunction with the 10th Paci?c-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The life sciences encompass research and development in areas such as bi- ogy, pharmacology, biophysics, biochemistry, neuroscience, medicine, and en- ronmental sciences. A common theme among life science disciplines is the desire to understand the stimuli-response mechanisms of biological entities, systems, and processes at di?erent levels of organization—from molecules to organisms to ecosystems. Asnaturalphenomenaarebeingprobedandmappedinever-greater detail, life scientists are generating an increasingly growing amount of textual information in the form of full-text research articles, abstracts, Web content, - ports, books, and so on. Even in well-focused subject areas it is becoming more and more di?cult for researchers and practitioners to ?nd, read, and process all textual information relevant to their tasks. Knowledge discovery in text (KDT) is a fast-developing ?eld that encompasses a variety of methodologies, me- ods and tools, which facilitate automated processing of text information stored in electronic format. KDT tasks that are particularly interesting to life science include: • Identi?cation and retrieval of relevant documents from one or more large collections of documents; • Identi?cation of relevant sections in large documents (passage retrieval); • Co-reference resolution, i. e.

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