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Mendeley readers
Chapter title |
Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives
|
---|---|
Chapter number | 2 |
Book title |
Computational Drug Discovery and Design
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7756-7_2 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7755-0, 978-1-4939-7756-7
|
Authors |
Abhigyan Nath, Priyanka Kumari, Radha Chaube |
Abstract |
Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions. |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 25 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Doctoral Student | 4 | 16% |
Student > Bachelor | 3 | 12% |
Researcher | 3 | 12% |
Student > Master | 2 | 8% |
Lecturer | 1 | 4% |
Other | 4 | 16% |
Unknown | 8 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 20% |
Biochemistry, Genetics and Molecular Biology | 3 | 12% |
Medicine and Dentistry | 2 | 8% |
Chemistry | 2 | 8% |
Decision Sciences | 1 | 4% |
Other | 3 | 12% |
Unknown | 9 | 36% |