Version 1
: Received: 25 June 2017 / Approved: 26 June 2017 / Online: 26 June 2017 (05:47:40 CEST)
How to cite:
Hirimutugoda, Y. Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks. Preprints2017, 2017060114. https://doi.org/10.20944/preprints201706.0114.v1
Hirimutugoda, Y. Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks. Preprints 2017, 2017060114. https://doi.org/10.20944/preprints201706.0114.v1
Hirimutugoda, Y. Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks. Preprints2017, 2017060114. https://doi.org/10.20944/preprints201706.0114.v1
APA Style
Hirimutugoda, Y. (2017). Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks. Preprints. https://doi.org/10.20944/preprints201706.0114.v1
Chicago/Turabian Style
Hirimutugoda, Y. 2017 "Time Evolving Undirected Graphical Model for Protein-Protein Interaction Networks" Preprints. https://doi.org/10.20944/preprints201706.0114.v1
Abstract
Proteins are the workhorses of the cell that perform biological functions by interacting with other proteins. Many statistical methods for protein-protein interaction (PPI) have been studied without considering time-dependent changes in networks and the functionalities. I introduced a novel method that models PPI networks as being dynamic in nature and evolving time-varying multivariate distribution with Conditional Random Fields (CRF). This research is directed towards implementing this new combinatorial algorithm on massively parallel architectures such as Graphics Processing Units (GPUs) for efficient computations for large scale bioinformatics datasets. I compared Conditional Random Fields (CRF) and the proposed novel method using CRF combined with the Block Coordinate Descent algorithm for human protein-protein interaction data set. Both are implemented on GPU-Accelerated Computing Architecture and the proposed novel method showed the advantages in predicting protein-protein interaction sites. I also show that the proposed approach is more efficient in 6.13% than standalone CRF++ in predicting protein-protein interaction sites.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
