Knowledge Discovery in Life Science Literature

International Workshop, KDLL 2006, Singapore, April 9, 2006, Proceedings
1. Auflage 2006
ISBN: 978-3-540-32809-4
Verlag: Springer

Buch, Englisch, Band 3886, 147 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 530 g

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, held in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 12 revised full papers presented together with two invited talks were carefully reviewed and selected for inclusion in the book. The papers cover all topics of knowledge discovery in life science data.

Zielgruppe

Research

Autoren/Hrsg.

Bremer, Eric G.

Hakenberg, Jörg

Han, Eui-Hong Sam

Berrar, Daniel

Dubitzky, Werner

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Biowissenschaften
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik

Inhaltsverzeichnis

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.