Title |
Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor
|
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Published in |
BMC Bioinformatics, March 2014
|
DOI | 10.1186/1471-2105-15-82 |
Pubmed ID | |
Authors |
Calem J Bendell, Shalon Liu, Tristan Aumentado-Armstrong, Bogdan Istrate, Paul T Cernek, Samuel Khan, Sergiu Picioreanu, Michael Zhao, Robert A Murgita |
Abstract |
Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods' restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Japan | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 4% |
Korea, Republic of | 2 | 3% |
Unknown | 72 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 17 | 22% |
Researcher | 11 | 14% |
Student > Master | 10 | 13% |
Student > Doctoral Student | 5 | 6% |
Student > Postgraduate | 5 | 6% |
Other | 10 | 13% |
Unknown | 19 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 18 | 23% |
Computer Science | 12 | 16% |
Biochemistry, Genetics and Molecular Biology | 11 | 14% |
Chemistry | 4 | 5% |
Medicine and Dentistry | 3 | 4% |
Other | 6 | 8% |
Unknown | 23 | 30% |