Title |
Efficient prediction of human protein-protein interactions at a global scale
|
---|---|
Published in |
BMC Bioinformatics, December 2014
|
DOI | 10.1186/s12859-014-0383-1 |
Pubmed ID | |
Authors |
Andrew Schoenrock, Bahram Samanfar, Sylvain Pitre, Mohsen Hooshyar, Ke Jin, Charles A Phillips, Hui Wang, Sadhna Phanse, Katayoun Omidi, Yuan Gui, Md Alamgir, Alex Wong, Fredrik Barrenäs, Mohan Babu, Mikael Benson, Michael A Langston, James R Green, Frank Dehne, Ashkan Golshani |
Abstract |
BackgroundOur knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.ResultsOn the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.ConclusionsThe speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 2% |
Australia | 1 | 2% |
Brazil | 1 | 2% |
Canada | 1 | 2% |
Spain | 1 | 2% |
Unknown | 61 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 24% |
Researcher | 14 | 21% |
Student > Master | 12 | 18% |
Student > Bachelor | 5 | 8% |
Student > Doctoral Student | 4 | 6% |
Other | 7 | 11% |
Unknown | 8 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 21 | 32% |
Agricultural and Biological Sciences | 15 | 23% |
Computer Science | 10 | 15% |
Medicine and Dentistry | 4 | 6% |
Engineering | 3 | 5% |
Other | 4 | 6% |
Unknown | 9 | 14% |