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TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

Yusong Liu, Xiufen Ye, Christina Y. Yu, Wei Shao, Jie Hou, Weixing Feng, Jie Zhang, Kun Huang
2021 BMC Bioinformatics  
Results In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks.  ...  Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.  ...  Correcting the Laplacian matrix by topology potential is necessary to improve the accuracy for module detection.  ... 
doi:10.1186/s12859-021-03964-5 pmid:34689740 fatcat:uzoxrblgkrh4ni7yfawup62c7i

Gene network interconnectedness and the generalized topological overlap measure

Andy M Yip, Steve Horvath
2007 BMC Bioinformatics  
Using theoretical arguments, a yeast coexpression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood  ...  The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules.  ...  The work was supported in parts by grant 1U19AI063603 01.  ... 
doi:10.1186/1471-2105-8-22 pmid:17250769 pmcid:PMC1797055 fatcat:i36lg3rhafgj3msrkuvbg5p5la

Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks

Marc R J Carlson, Bin Zhang, Zixing Fang, Paul S Mischel, Steve Horvath, Stanley F Nelson
2006 BMC Genomics  
Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function.  ...  The constructed networks approximate scale-free topology, but this is not universal across all datasets.  ...  Acknowledgements The authors extend their thanks to the SGD for providing data for this analysis.  ... 
doi:10.1186/1471-2164-7-40 pmid:16515682 pmcid:PMC1413526 fatcat:r4xoerkeibeozm5vae3ln2jjcm

Exploiting locational and topological overlap model to identify modules in protein interaction networks

Lixin Cheng, Pengfei Liu, Dong Wang, Kwong-Sak Leung
2019 BMC Bioinformatics  
Conclusion: Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.  ...  LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins.  ...  The topological overlap matrix model (TOM) In this part, we introduce the model of Topological Overlap Matrix (TOM) based on the work of Yip et al. [8] .  ... 
doi:10.1186/s12859-019-2598-7 fatcat:ff5mzskb7ffw7asmc4meq7d33a

Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse

Ovidiu D Iancu, Priscila Darakjian, Nicole AR Walter, Barry Malmanger, Denesa Oberbeck, John Belknap, Shannon McWeeney, Robert Hitzemann
2010 BMC Genomics  
As measured by the WGCNA, the modular structure of the transcriptome networks was also preserved both on the basis of module assignment and from the perspective of the topological overlap maps.  ...  Gene ontology annotation of the modules revealed a significant overrepresentation in some modules for neuronal processes, e.g., central nervous system development.  ...  Knight Cancer Institute [5] [6] [7] [8] [9] [10] [11] [12] [13] , and grant support from the Department of Veterans Affairs.  ... 
doi:10.1186/1471-2164-11-585 pmid:20959017 pmcid:PMC3091732 fatcat:5zry5xwmnnfflb7n5jum2pqvla

Network module detection: Affinity search technique with the multi-node topological overlap measure

Ai Li, Steve Horvath
2009 BMC Research Notes  
Findings: We adapt network neighborhood analysis for the use of module detection.  ...  Here, we define modules as clusters of network nodes with high multi-node topological overlap.  ...  The authors would like to thank UCLA collaborators Peter Langfelder, Jun Dong, Tova Fuller, Jake Lusis, Tom Drake, Dan Geschwind, Wen Lin, Paul Mischel, Mike Oldham, Anja Presson, and Wei Zhao for useful  ... 
doi:10.1186/1756-0500-2-142 pmid:19619323 pmcid:PMC2727520 fatcat:csncrstb7nahbjwsi2646wepza

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks [article]

Emily Greenfest-Allen, Jean-Philippe Cartailler, Mark A Magnuson, Christian J Stoeckert
2017 bioRxiv   pre-print
Here we present iterativeWGCNA, a Python-wrapped extension for the WGCNA R software package that improves the robustness of detected modules and minimizes information loss.  ...  After refining, pruned genes are assembled into a new expression dataset to isolate overlapping modules and the process repeated.  ...  Acknowledgements This study was supported by funding from the NIH to MAM (DK72473 and DK89523).  ... 
doi:10.1101/234062 fatcat:quhqslpr3bbnfhqm7n2yjmplgi

MODA: MOdule Differential Analysis for weighted gene co-expression network [article]

Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu, Shan He
2016 arXiv   pre-print
Taking the network as a collection as modules, we use a sample-saving method to construct condition-specific gene co-expression network, and identify differentially expressed subnetworks as conserved or  ...  By comparing different gene co-expression networks we may find conserved part as well as condition specific set of genes.  ...  The module detection in WGCNA is based on hierarchical clustering, which groups similar genes into one cluster. The similarity was defined by topological overlap measure [2] .  ... 
arXiv:1605.04739v1 fatcat:bxaru32bhjfq5oo4tcl6iuoqqy

Comparison and evaluation of network clustering algorithms applied to genetic interaction networks

Lin Hou
2012 Frontiers in Bioscience (Elite Edition)  
Genes can then be clustered through hierarchical clustering, with topological overlap matrix as the similarity matrix.  ...  In gene co-expression networks, hierarchical clustering (21), topological overlap matrix (22) , and bi-clustering (23) are effective algorithms in predicting co-regulated gene sets.  ... 
doi:10.2741/532 pmid:22202027 fatcat:gyubis6o4vantbvwwzh2m5c2du

DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

Bruno M Tesson, Rainer Breitling, Ritsert C Jansen
2010 BMC Bioinformatics  
Results: We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression  ...  We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.  ...  We thank Jackie Senior for editing this article. Authors' contributions BMT designed and implemented the algorithm, analyzed the results and drafted the manuscript.  ... 
doi:10.1186/1471-2105-11-497 pmid:20925918 pmcid:PMC2976757 fatcat:lah72netd5grlhmguqv4nmvuei

Network analysis of drug effect on triglyceride-associated DNA methylation

Elise Lim, Hanfei Xu, Peitao Wu, Daniel Posner, Jiayi Wu, Gina M. Peloso, Achilleas N. Pitsillides, Anita L. DeStefano, L. Adrienne Cupples, Ching-Ti Liu
2018 BMC Proceedings  
We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre-and posttreatment.  ...  For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules.  ...  It would be more telling if we could use a two-step or even more general topological overlap rather than one-step topological overlap to include more information about the neighboring structure around  ... 
doi:10.1186/s12919-018-0130-0 pmid:30275881 pmcid:PMC6157190 fatcat:75ewmcedqbfihcroc2x4rlrt4e

An effective method for network module extraction from microarray data

Priyakshi Mahanta, Hasin A Ahmed, Dhruba K Bhattacharyya, Jugal K Kalita
2012 BMC Bioinformatics  
Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules.  ...  Results: This paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network.  ...  The full contents of the supplement are available online at  ... 
doi:10.1186/1471-2105-13-s13-s4 pmid:23320896 pmcid:PMC3426802 fatcat:yvmndjtygjgevp3bm3hz3xu4rm

A General Framework for Weighted Gene Co-Expression Network Analysis

Bin Zhang, Steve Horvath
2005 Statistical Applications in Genetics and Molecular Biology  
Second, we provide theoretical and empirical evidence that the 'weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its 'unweighted' counterpart.  ...  For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion).We generalize the following important network  ...  Acknowledgement We would like to acknowledge the grant support from NINDS/NIMH 1U24NS043562-01 (PI Stanley Nelson).  ... 
doi:10.2202/1544-6115.1128 pmid:16646834 fatcat:73adb4tuxvfxxaeys2ibteacqy

Understanding network concepts in modules

Jun Dong, Steve Horvath
2007 BMC Systems Biology  
matrix has been used to define modules and to annotate genes.  ...  For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap  ...  Acknowledgements We would like to acknowledge the grant support from Program Project Grant 1U19AI063603-01 and NINDS/NIMH 1U24NS043562-01.  ... 
doi:10.1186/1752-0509-1-24 pmid:17547772 pmcid:PMC3238286 fatcat:wsd2ngjafbhmlewvrnts5kshli

ModularBoost: an efficient network inference algorithm based on module decomposition

Xinyu Li, Wei Zhang, Jianming Zhang, Guang Li
2021 BMC Bioinformatics  
To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods.  ...  Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively.  ...  Acknowledgements The authors are grateful preliminary research on GRN inference from Wenchao Li, which sparked the idea for this project.  ... 
doi:10.1186/s12859-021-04074-y pmid:33761871 fatcat:5nfj4uyh5zagpfgzrkps6wgqqu
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