Accelerating discovery mining unstructured information for hypothesis generation
Unstructured Mining Approaches to Solve Complex Scientific ProblemsAs the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and i...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
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Boca Raton :
CRC Press, is an imprint of the Taylor & Francis Group, an Informa business
[2016]
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| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC data mining and knowledge discovery series.
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630057006719 |
Tabla de Contenidos:
- chapter 1. Introduction
- chapter 2. Why accelerate discover? / Scott Spangler and Ying Chen
- chapter 3. Form and function
- chapter 4. Exploring content to find entities
- chapter 5. Organization
- chapter 6. Relationships
- chapter 7. Inference
- chapter 8. Taxonomies
- chapter 9. Orthogonal comparison
- chapter 10. Visualizing the data plane
- chapter 11. Networks
- chapter 12. Examples and problems
- chapter 13. Problem : discovery of novel properties of known entities
- chapter 14. Problem : finding new treatments for orphan diseases from existing drugs
- chapter 15. Example : target selection based on protein network analysis
- chapter 16. Example : gene expression analysis for alternative indications
- chapter 17. Example : side effects
- chapter 18. Example : protein viscosity analysis using medline abstracts
- chapter 19. Example : finding microbes to clean up oil spills / Scott Spangler, Zarath Summers, and Adam Usadi
- chapter 20. Example : drug repurposing
- chapter 21. Example : adverse events
- chapter 22. Example : P53 kinases
- chapter 23. Conclusion and future work.
