Mining the biomedical literature /

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A concise introduction to fundamental methods for finding and extracting relevant information from the ever-increasing amounts of biomedical text available. The introduction of high-throughput methods has transformed biology into a data-rich science. Knowledge about biological entities and processes...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Shatkay, Hagit (Autor), Craven, Mark (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2012.
Colección:Computational molecular biology.
Temas:
Medical literature > Data processing.
Biological literature > Data processing.
Data mining.
Medical informatics.
Bioinformatics.
Information storage and retrieval systems > Medicine.
Information storage and retrieval systems > Biology.
Content analysis (Communication)
Information retrieval.
Data Mining
Information Storage and Retrieval
Medical Informatics
Computational Biology
Médecine > Documentation > Informatique.
Biologie > Documentation > Informatique.
Exploration de données (Informatique)
Médecine > Informatique.
Bio-informatique.
Systèmes d'information > Médecine.
Systèmes d'information > Biologie.
Analyse de contenu (Communication)
Recherche de l'information.
subject analysis.
information retrieval.
MEDICAL > Allied Health Services > Medical Technology.
MEDICAL > Biotechnology.
MEDICAL > Family & General Practice.
MEDICAL > Lasers in Medicine.
TECHNOLOGY & ENGINEERING > Biomedical.
Bioinformatics
Data mining
Information retrieval
Information storage and retrieval systems > Biology
Information storage and retrieval systems > Medicine
Medical informatics
Medical literature > Data processing
BIOMEDICAL SCIENCES/Quantitative Biology
BIOMEDICAL SCIENCES/General
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Fundamental Concepts in Biomedical Text Analysis
  • Information Retrieval
  • Information Extraction
  • Evaluation
  • Putting it All Together : Current Applications and Future Directions.
  • Intro
  • Contents
  • Acknowledgments
  • Chapter 1. Introduction
  • 1.1. What is biomedical text mining?
  • 1.2. Example: The BRCA1 Pathway
  • 1.3. Challenges in biomedical text mining
  • Chapter 2. Fundamental Concepts in Biomedical Text Analysis
  • 2.1. Biomedical text sources
  • 2.2. Natural language concepts
  • 2.3. Challenges in natural language processing
  • 2.4. Natural language processing tasks
  • 2.5. Biomedical vocabularies and ontologies
  • 2.6. Summary
  • Chapter 3. Information Retrieval
  • 3.1. Example : The BRCA1 Pathway (Revisited)
  • 3.2. Indexing, keywords, and Boolean queries
  • 3.3. Similarity queries and the Vector Model
  • 3.4. Beyond cosine-based similarity
  • 3.5. Text categorization
  • 3.6. Summary
  • Chapter 4. Information Extraction
  • 4.1. Named-entity recognition
  • 4.2. Normalization of named entities
  • 4.3. Relation extraction
  • 4.4. Summary
  • Chapter 5. Evaluation
  • 5.1. Performance evaluation in text retrieval and extraction
  • 5.2. Evaluation measures
  • 5.3. Shared evaluation tasks
  • 5.4. Summary
  • Chapter 6 Putting It All Together : Current Applications and Future Directions
  • 6.1. Recognizing and linking bioentities
  • 6.2. Supporting database curation
  • 6.3. Text as data : A gateway to discovery and prediction
  • 6.4. Future directions
  • References
  • Index.

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