iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/26436504
Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Oct 2;14(4):11791-805.
doi: 10.4238/2015.October.2.13.

Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm

J H Jia  1 , Z Liu  2 , X Chen  2 , X Xiao  2 , B X Liu  2
Affiliations
Free article

Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm

J H Jia et al. Genet Mol Res. .
Free article

Abstract

Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

Publication types

MeSH terms

Substances

LinkOut - more resources