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.

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.

A network-based approach reveals novel invasion and Maurer's clefts-related proteins in Plasmodium falciparum

2019 ◽  
Vol 15 (6) ◽  
pp. 431-441 ◽  
Author(s):  
Dibyajyoti Das ◽  
Sowmya Ramaswamy Krishnan ◽  
Arijit Roy ◽  
Gopalakrishnan Bulusu
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Disease Pathogenesis ◽  
Protein Protein Interaction ◽  
Novel Proteins ◽  
Maurer's Clefts ◽  
Related Proteins ◽  
Protein Protein Interaction Network

To understand disease pathogenesis, all the disease-related proteins must be identified. In this work, known proteins were used to identify related novel proteins using RWR method on a dynamic P. falciparum protein–protein interaction network.

CORRELATION BETWEEN FUNCTIONAL ANNOTATION AND TOPOLOGY OF PROTEIN-PROTEIN INTERACTION NETWORK

2008 ◽  
Vol 22 (06) ◽  
pp. 719-726 ◽  
Author(s):  
JIUN-YAN HUANG
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Protein Function ◽  
Functional Annotation ◽  
Interaction Network ◽  
Protein Interaction Data ◽  
Characteristic Distance ◽  
Protein Protein Interaction ◽  
Functional Correlation ◽  
Protein Protein Interaction Network

The functional annotation of proteins was believed to be related to the topology of the protein-protein interaction network. People utilized the protein-protein interaction network to infer the protein function by various methods. Here, we select the protein interaction data of Saccharomyces cerevisia and calculated the correlation between functional annotation of proteins and the topology of protein-protein interaction network. The result shows that the functional correlation decays exponentially with the distance between two proteins, and beyond the characteristic distance, it has no correlation.

scholarly journals A scored human protein-protein interaction network to catalyze genomic interpretation

2016 ◽  
Author(s):  
T Li ◽  
R Wernersson ◽  
RB Hansen ◽  
H Horn ◽  
JM Mercer ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Cell Biology ◽  
Large Scale ◽  
Interaction Network ◽  
Human Protein ◽  
Functional Interpretation ◽  
Protein Protein Interaction ◽  
Neuropsychiatric Diseases ◽  
Protein Protein Interaction Network

Human protein-protein interaction networks are critical to understanding cell biology and interpreting genetic and genomic data, but are challenging to produce in individual large-scale experiments. We describe a general computational framework that through data integration and quality control provides a scored human protein-protein interaction network (InWeb_IM). Juxtaposed with five comparable resources, InWeb_IM has 2.8 times more interactions (~585K) and a superior functional signal showing that the added interactions reflect real cellular biology. InWeb_IM is a versatile resource for accurate and cost-efficient functional interpretation of massive genomic datasets illustrated by annotating candidate genes from >4,700 cancer genomes and genes involved in neuropsychiatric diseases.

Protein–protein interaction network with machine learning models and multiomics data reveal potential neurodegenerative disease-related proteins

2020 ◽  
Vol 29 (8) ◽  
pp. 1378-1387 ◽  
Author(s):  
Xinjian Yu ◽  
Siqi Lai ◽  
Hongjun Chen ◽  
Ming Chen
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Computational Study ◽  
Interaction Network ◽  
Model Organisms ◽  
Protein Protein Interaction ◽  
Related Proteins ◽  
Protein Protein Interaction Network

Abstract Research of protein–protein interaction in several model organisms is accumulating since the development of high-throughput experimental technologies and computational methods. The protein–protein interaction network (PPIN) is able to examine biological processes in a systematic manner and has already been used to predict potential disease-related proteins or drug targets. Based on the topological characteristics of the PPIN, we investigated the application of the random forest classification algorithm to predict proteins that may cause neurodegenerative disease, a set of pathological changes featured by protein malfunction. By integrating multiomics data, we further showed the validity of our machine learning model and narrowed down the prediction results to several hub proteins that play essential roles in the PPIN. The novel insights into neurodegeneration pathogenesis brought by this computational study can indicate promising directions for future experimental research.

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.

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