Inferring pleiotropy by network analysis: linked diseases in the human PPI network

Nguyễn, Thanh Phương; Liu, Wei-Chung; Jordán, Ferenc

doi:10.1186/1752-0509-5-179

Cited by 61 publications

(46 citation statements)

References 34 publications

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“…Proteins involved in the aging process are often bridges (Wang et al, 2009). Proteins bridging disease modules may provide important points of interventions (Nguyen & Jordán, 2010; Nguyen et al, 2011). …”

Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

“…These core modules were often targeted by drugs, and drugs affecting the core modules were more often multi-target drugs than those acting on ‘peripheral’ modules, which changed their mRNA levels only in a few specific diseases. Bridges and additional types of overlaps between disease-related interactome modules may provide important points of interventions (Nguyen & Jordán, 2010; Nguyen et al, 2011). …”

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

“…Refinement of interactome data containing domain-domain interactions combined with the earlier observation that disease-related genes have a smaller than average clustering coefficient (Feldman et al, 2008) led to the prediction of type 2 diabetes-related genes (Sharma et al, 2010a). Inter-modular interactome nodes between type 2 diabetes-, obesity- and heart disease-related proteins may play a key role in the dysregulation of these complex syndromes (Nguyen & Jordán, 2010; Nguyen et al, 2011). …”

Section: Four Examples Of Network Description and Analysis In Drugmentioning

confidence: 99%

“…Genes bridging disease modules in the human interactome may provide important points of interventions (Nguyen & Jordán, 2010; Nguyen et al, 2011). Genes involved in the aging process often occupy such bridging positions (Wang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

Section: Four Examples Of Network Description and Analysis In Drugmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

View full text Add to dashboard Cite

show abstract

“…These responsibilities are performed by the interaction between proteins. Therefore, prediction of PPI improves the knowledge of the cell functionality, protein functions [2][3][4], gene functions [5], signaling pathway [6] and disease proteins finding [7].…”

Section: Introductionmentioning

confidence: 99%

Protein-Protein Interaction Prediction using PCA and SVR-PHCS

Mahmoudian¹,

Yousef²,

Charkari³

2015

TOBIOIJ

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Protein-Protein Interactions (PPIs) play a key role in many biological systems. Thus, identifying PPIs is critical for understanding cellular processes. Many experimental techniques were applied to predict PPIs. The data extracted using these techniques are incomplete and noisy. In this regard, a number of computational methods include machine learning classification techniques have been developed to reduce the noise data and predict new PPIs.Since, using regression methods to solve classification problems has good results in other applications. Therefore, in this paper, a regression view is applied to the PPI prediction classification problem, so a new approach is proposed using Principal Component Analysis (PCA) and Support Vector Regression (SVR) which has been improved by a new Parallel Hierarchical Cube Search (PHCS) method. Firstly, PCA algorithm is implemented to select an optimal subset of features which leads to reduce processing time and to lessen the effect of noise. Then, the PPIs would be predicted, by using SVR. To get a better performance of SVR, a new PHCS method has been applied to select the appropriate values of SVR parameters. The obtained classification accuracy of the proposed method is 74.505% on KUPS (The University of Kansas Proteomics Service) dataset which outperforms the other methods.

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Molecular models of the cardiorenal syndrome

et al. 2013

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Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross-organ mediators and common pathophysiology. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term-specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term-specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi-omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.

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Inferring pleiotropy by network analysis: linked diseases in the human PPI network

Cited by 61 publications

References 34 publications

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Protein-Protein Interaction Prediction using PCA and SVR-PHCS

Molecular models of the cardiorenal syndrome

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