Cited 2 time in webofscience Cited 2 time in scopus

TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information

Title
TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information
Authors
Kim, Y[Kim, Yongsoo]Jang, JH[Jang, Jin-Hyeok]Choi, S[Choi, Seungjin]Hwang, D[Hwang, Daehee]
DGIST Authors
Hwang, D[Hwang, Daehee]
Issue Date
2014-09-01
Citation
Bioinformatics, 30(17), I453-I460
Type
Article
Article Type
Article; Proceedings Paper
Keywords
Cell CycleGene ExpressionModels, StatisticalProceduresProtein AnalysisProtein Interaction MappingStatistical Model
ISSN
1367-4803
Abstract
Motivation: Time-evolving differential protein-protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up-or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations.. © The Author(s) 2014.
URI
http://hdl.handle.net/20.500.11750/2379
DOI
10.1093/bioinformatics/btu454
Publisher
Oxford University Press
Related Researcher
  • Author Hwang, Dae Hee Systems Biology and Medicine Lab
  • Research Interests Multilayered spatiotemporal networks; Regulatory motifs or pathways; Metabolite-protein networks; Network stochasticity; Proteomics and informatics
Files:
There are no files associated with this item.
Collection:
New BiologyETC1. Journal Articles


qrcode mendeley

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

BROWSE