Data Integration in the Life Sciences 7th International Conference, DILS 2010, Gothenburg, Sweden, August 25-27, 2010. Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Lambrix, Patrick (Επιμελητής έκδοσης), Kemp, Graham (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Lecture Notes in Computer Science, 6254
• Θέματα: Life sciences.; Computer communication systems.; Information storage and retrieval.; Artificial intelligence.; Computer engineering.; Life Sciences.; Life Sciences, general.; Computer Engineering.; Computer Communication Networks.; Information Systems Applications (incl. Internet).; Artificial Intelligence (incl. Robotics).; Information Storage and Retrieval.

The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglyd...

Πίνακας περιεχομένων:

• Invited Talks
• Provenance Management for Data Exploration
• High-Performance Systems for in Silico Microscopy Imaging Studies
• Ontology Engineering
• Discovering Evolving Regions in Life Science Ontologies
• On Matching Large Life Science Ontologies in Parallel
• A System for Debugging Missing Is-a Structure in Networked Ontologies
• Web Services
• On the Secure Sharing and Aggregation of Data to Support Systems Biology Research
• Helping Biologists Effectively Build Workflows, without Programming
• A Data Warehouse Approach to Semantic Integration of Pseudomonas Data
• Data Mining and Text Mining
• The Cinderella of Biological Data Integration: Addressing Some of the Challenges of Entity and Relationship Mining from Patent Sources
• Algorithm for Grounding Mutation Mentions from Text to Protein Sequences
• Handling Missing Features with Boosting Algorithms for Protein–Protein Interaction Prediction
• Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integration
• Information Management
• Integrative Information Management for Systems Biology
• An Integration Architecture Designed to Deal with the Issues of Biological Scope, Scale and Complexity
• Quality Assessment of MAGE-ML Genomic Datasets Using DescribeX
• Search Computing: Integrating Ranked Data in the Life Sciences.