ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network

Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang‐Xiang; Pan, Yi

doi:10.1109/tcbb.2014.2361348

Cited by 121 publications

(93 citation statements)

References 44 publications

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“…The generated network was analyzed using network analysis tools implemented in the Cytoscape software package (63). Network clustering was evaluated using a fast agglomerative algorithm based on edge clustering (FAG-EC) implemented in ClusterViz plug-in of Cytoscape (64). The complex size threshold was set to 10.…”

Section: Data Acquisition and Peptide Identification Protein Abundancymentioning

confidence: 99%

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

Borovok

Nesher

Levin

et al. 2016

Molecular & Cellular Proteomics

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Spatial memory depends on the hippocampus, which is particularly vulnerable to aging. This vulnerability has implications for the impairment of navigation capacities in older people, who may show a marked drop in performance of spatial tasks with advancing age. Contemporary understanding of long-term memory formation relies on molecular mechanisms underlying long-term synaptic plasticity. With memory acquisition, activity-dependent changes occurring in synapses initiate multiple signal transduction pathways enhancing protein turnover. This enhancement facilitates de novo synthesis of plasticity related proteins, crucial factors for establishing persistent long-term synaptic plasticity and forming memory engrams. Extensive studies have been performed to elucidate molecular mechanisms of memory traces formation; however, the identity of plasticity related proteins is still evasive. In this study, we investigated protein turnover in mouse hippocampus during long-term spatial memory formation using the reference memory version of radial arm maze (RAM) paradigm. We identified 1592 proteins, which exhibited a complex picture of expression changes during spatial memory formation. Variable linear decomposition reduced significantly data dimensionality and enriched three principal factors responsible for variance of memory-related protein levels at (1) the initial phase of memory acquisition (165 proteins), (2) during the steep learning improvement (148 proteins), and (3) the final phase of the learning curve (123 proteins). Gene ontology and signaling pathways analysis revealed a clear correlation between memory improvement and learning phasecurbed expression profiles of proteins belonging to specific functional categories. We found differential enrichment of (1) Long-term synaptic plasticity is considered a cellular correlate of long-term memory (LTM) 1 . Contemporary understanding of memory formation is based on the initiation and maintenance of long-term synaptic plasticity (1-4), for which de novo protein synthesis is a vital requirement. De novo protein synthesis itself is secondary to activity-dependent changes in synapses that occur during learning processes. These activity changes trigger post-translational modifications of proteins initiating and sustaining multiple signal transduction pathways. In turn, these signaling pathways regulate changes in synaptic strength and connectivity by governing gene expression and protein translation (5-13). Depending on time elapsed since triggering of long-term synaptic plasticity, protein synthesis may be limited to the dendrites directly involved in the plasticity processes (14 -18). Multiple synaptic From the ‡Department

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Section: Data Acquisition and Peptide Identification Protein Abundancymentioning

confidence: 99%

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

Borovok

Nesher

Levin

et al. 2016

Molecular & Cellular Proteomics

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“…It is expected that additional information may improve prediction performance. In the future, more information using other methods such as ClusterViz [57] should be integrated to develop a more efficient prediction method. Nevertheless, this study provides an important basis for new drug development and drug repositioning and also plays an important role in the personalized medical development.…”

Section: Discussionmentioning

confidence: 99%

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Cheng

Lan

et al. 2017

Complexity

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It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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“…Functional modules of CePIN were first explored by the FAG-EC algorithm, which could be used for large-scale PIN and showed hierarchical structure of functional modules by modifying the parameters [58]. Then the identified modules were used for functional enrichment analysis by the BinGO plugin for Cytoscape.…”

Section: Methodsmentioning

confidence: 99%

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

Huo

Wang

et al. 2017

IJMS

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Salvia miltiorrhiza, known as Danshen, has attracted worldwide interest for its substantial effects on coronary heart disease (CHD). Danshensu (DSS) is one of the main active ingredients of Danshen on CHD. Although it has been proven to have a good clinical effect on CHD, the action mechanisms remain elusive. In the current study, a coexpression network-based approach was used to illustrate the beneficial properties of DSS in the context of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs) data, two coexpression protein interaction networks (CePIN) in a CHD state (CHD CePIN) and a non-CHD state (non-CHD CePIN) were generated. Then, shared nodes and unique nodes in CHD CePIN were attained by conducting a comparison between CHD CePIN and non-CHD CePIN. By calculating the topological parameters of each shared node and unique node in the networks, and comparing the differentially expressed genes, target proteins involved in disease regulation were attained. Then, Gene Ontology (GO) enrichment was utilized to identify biological processes associated to target proteins. Consequently, it turned out that the treatment of CHD with DSS may be partly attributed to the regulation of immunization and blood circulation. Also, it indicated that sodium/hydrogen exchanger 3 (SLC9A3), Prostaglandin G/H synthase 2 (PTGS2), Oxidized low-density lipoprotein receptor 1 (OLR1), and fibrinogen gamma chain (FGG) may be potential therapeutic targets for CHD. In summary, this study provided a novel coexpression protein interaction network approach to provide an explanation of the mechanisms of DSS on CHD and identify key proteins which maybe the potential therapeutic targets for CHD.

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ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network

Cited by 121 publications

References 44 publications

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

Dynamics of Hippocampal Protein Expression During Long-term Spatial Memory Formation

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

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