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Mendeley readers
Chapter title |
Prediction of Cell-Penetrating Peptides
|
---|---|
Chapter number | 3 |
Book title |
Cell-Penetrating Peptides
|
Published in |
Methods in molecular biology, January 2015
|
DOI | 10.1007/978-1-4939-2806-4_3 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2805-7, 978-1-4939-2806-4
|
Authors |
Hällbrink, Mattias, Karelson, Mati, Mattias Hällbrink, Mati Karelson |
Editors |
Langel, Ülo |
Abstract |
The in silico methods for the prediction of the cell-penetrating peptides are reviewed. Those include the multivariate statistical methods, machine-learning methods such as the artificial neural networks and support vector machines, and molecular modeling techniques including molecular docking and molecular dynamics.The applicability of the methods is demonstrated on the basis of the exemplary cases from the literature. |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Uruguay | 1 | 2% |
Unknown | 42 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 26% |
Researcher | 8 | 19% |
Student > Bachelor | 5 | 12% |
Student > Doctoral Student | 3 | 7% |
Professor > Associate Professor | 3 | 7% |
Other | 7 | 16% |
Unknown | 6 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 8 | 19% |
Medicine and Dentistry | 7 | 16% |
Chemistry | 7 | 16% |
Biochemistry, Genetics and Molecular Biology | 6 | 14% |
Engineering | 4 | 9% |
Other | 4 | 9% |
Unknown | 7 | 16% |