Chapman & Hall/CRC data mining and knowledge discovery series, 37.
GENERAL NOTES
Text of Note
A Chapman & Hall book.
CONTENTS NOTE
Text of Note
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.
SUMMARY OR ABSTRACT
Text of Note
Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses.
TOPICAL NAME USED AS SUBJECT
Data mining.
Science -- Information resources.
Science -- Methodology.
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
9
.
D343
Book number
S368
9999
PERSONAL NAME - PRIMARY RESPONSIBILITY
Scott Spangler, IBM research, San Jose, California, USA.