An integrative C. elegans protein–protein interaction network with reliability assessment based on a probabilistic graphical model

2016 ◽  
Vol 12 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Xiao-Tai Huang ◽  
Yuan Zhu ◽  
Leanne Lai Hang Chan ◽  
Zhongying Zhao ◽  
Hong Yan
Keyword(s):  
Protein Interaction ◽  
Graphical Model ◽  
Reliability Assessment ◽  
Interaction Network ◽  
Probabilistic Graphical Model ◽  
Ppi Network ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Reliability Score ◽  
Protein Protein Interaction Network

We construct an integrative protein–protein interaction (PPI) network in Caenorhabditis elegans, which is weighted by our proposed reliability score based on a probability graphical model (RSPGM) method.

scholarly journals MOfinder: A Novel Algorithm for Detecting Overlapping Modules from Protein-Protein Interaction Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Qi Yu ◽  
Gong-Hua Li ◽  
Jing-Fei Huang
Keyword(s):  
Protein Interaction ◽  
Cell Biology ◽  
Functional Annotation ◽  
Immune Therapy ◽  
Interaction Network ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
New Perspective ◽  
Related Proteins ◽  
Protein Protein Interaction Network

Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein-protein interaction network

2021 ◽  
Author(s):  
Backiyarani Suthanthiram ◽  
Sasikala Rajendran ◽  
Sharmiladevi Simeon ◽  
Uma Subbaraya
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Banana, one of the most important staple, delicious fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein-protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By exploring the PPI of candidate genes from the putative network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLV-WUSHEL signaling pathway in addition to gibberellin mediated auxin signaling pathway in parthenocarpy. Further validation of candidate genes in seeded and seedless accession of Musa spp using qRT-PCR put forward AGL8, MADS16, IAA (GH3.8), RGA1, EXPA1, GID1C, HK2 and BAM1 as possible target genes in natural parthenocarpy. In contrary, expression profile of ACLB-2 and ZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through protein-protein interaction network.

scholarly journals Statistical Approaches for the Construction and Interpretation of Human Protein-Protein Interaction Network

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yang Hu ◽  
Ying Zhang ◽  
Jun Ren ◽  
Yadong Wang ◽  
Zhenzhen Wang ◽  
...  
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Latent Variable ◽  
Interaction Network ◽  
Human Protein ◽  
Ppi Network ◽  
Confidence Measure ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

The overall goal is to establish a reliable human protein-protein interaction network and develop computational tools to characterize a protein-protein interaction (PPI) network and the role of individual proteins in the context of the network topology and their expression status. A novel and unique feature of our approach is that we assigned confidence measure to each derived interacting pair and account for the confidence in our network analysis. We integrated experimental data to infer human PPI network. Our model treated the true interacting status (yes versus no) for any given pair of human proteins as a latent variable whose value was not observed. The experimental data were the manifestation of interacting status, which provided evidence as to the likelihood of the interaction. The confidence of interactions would depend on the strength and consistency of the evidence.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network
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