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Mendeley readers
Chapter title |
Compound Data Mining for Drug Discovery.
|
---|---|
Chapter number | 14 |
Book title |
Bioinformatics
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Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6613-4_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
Authors |
Jürgen Bajorath |
Editors |
Jonathan M. Keith |
Abstract |
In recent years, there has been unprecedented growth in compound activity data in the public domain. These compound data provide an indispensable resource for drug discovery in academic environments as well as in the pharmaceutical industry. To handle large volumes of heterogeneous and complex compound data and extract discovery-relevant knowledge from these data, advanced computational mining approaches are required. Herein, major public compound data repositories are introduced, data confidence criteria reviewed, and selected data mining approaches discussed. |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 3 | 19% |
Other | 2 | 13% |
Student > Ph. D. Student | 2 | 13% |
Researcher | 1 | 6% |
Professor > Associate Professor | 1 | 6% |
Other | 1 | 6% |
Unknown | 6 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 3 | 19% |
Computer Science | 3 | 19% |
Biochemistry, Genetics and Molecular Biology | 1 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
Agricultural and Biological Sciences | 1 | 6% |
Other | 0 | 0% |
Unknown | 7 | 44% |