Please use this identifier to cite or link to this item:
Title: Amino-acid residue association models for large scale protein-protein interaction prediction
Authors: Rao, R. 
Tun, K.
Makita, Y.
Lakshminarayanan, S. 
Dhar, P.K.
Keywords: Amino acid residue
Computational method
Machine learning
Pattern recognition
Protein-protein interaction
Systems biology
Issue Date: 2009
Citation: Rao, R.,Tun, K.,Makita, Y.,Lakshminarayanan, S.,Dhar, P.K. (2009). Amino-acid residue association models for large scale protein-protein interaction prediction. In Silico Biology 9 (4) : 179-194. ScholarBank@NUS Repository.
Abstract: The computational prediction of protein-protein interactions (PPI) is an essential complement to direct experimental evidence. Traditional approaches rely on less available or computationally predicted surface properties, show database-specific performances and are computationally expensive for large-scale datasets. Several sensitivity and specificity issues remain. Here, we report a novel method based on 'Amino-acid Residue Associations' (ARA) among interacting proteins which utilizes the accurate and easily available primary sequence. Large scale PPI datasets for six model species (from E. coli to human) were studied. The ARA method shows up to 73% sensitivity and 78% specificity. Furthermore, the method performs remarkably well in terms of stability and generalizability. The performance of ARA method benchmarked against existing prediction techniques shows performance improvement up to 25%. Ability of ARA method to predict PPI across species and across databases is also demonstrated. Overall, the ARA method provides a significant improvement over existing ones in correctly identifying large scale protein-protein interactions, irrespective of the data resource, network size or organism. © 2009 IOS Press. All rights reserved.
Source Title: In Silico Biology
ISSN: 14343207
DOI: 10.3233/ISB-2009-0397
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jul 19, 2019

Page view(s)

checked on May 22, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.