Integrative analysis of human protein, function and disease networks

Liu, Wei; Wu, Aiping; Pellegrini, Matteo; Wang, Xiaofan

doi:10.1038/srep14344

Cited by 34 publications

(24 citation statements)

References 61 publications

(120 reference statements)

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“…(1) node that represents protein and (2) edge that refers to interaction (Figure 1). PPI network has been applied for evolutionary study [45], gene/protein functional prediction [46], and also pathobiology of diseases [47,48]. There are few analyses that can be applied using the PPI network approach, and PPI network topological analysis is one of the analyses that often are used to study the pathobiology of human diseases.…”

Section: Protein-protein Interaction Analysismentioning

confidence: 99%

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Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Afiqah‐Aleng¹,

Mohamed‐Hussein²

2020

Polycystic Ovarian Syndrome

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Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS.

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Section: Protein-protein Interaction Analysismentioning

confidence: 99%

“…Clustering is also a technique in network analysis in human diseases. A cluster refers to a small group that has similar topological network properties [48,54]. In this method, it is hypothesized that the proteins in a module tend to be associated with the same diseases.…”

Section: Protein-protein Interaction Analysismentioning

confidence: 99%

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Afiqah‐Aleng¹,

Mohamed‐Hussein²

2020

Polycystic Ovarian Syndrome

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“…Finally, in a network approach to the problem, one constructs a (weighted) symptom-disease network with connections relating the signs to the diseases. This network along with other complementary data, e.g., genedisease, RNA-disease, protein-disease, metabolite-disease and disease-disease networks, are then utilized as information resources by a diagnostic algorithm [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Statistical physics of medical diagnostics: Study of a probabilistic model

Mashaghi

Ramezanpour

2018

Phys. Rev. E

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We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

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“…In drug discovery, network pharmacology paradigms are often applied to understand the underlying pharmacological mechanism of action of a given drug to certain disease [23][24][25][26]. Recently, omic technologies and systems biology have been adopted to predict the combinatory drug effect in order to understand their pharmacological mechanism based on target analysis [27]. Furthermore, in networks pharmacology, both technologies are often integrated to reveal the relationship between drugs and their targets [28].…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Action Mechanism of Indonesia Herbal Decoction in the Treatment of Type Ii Diabetes Using a Network Pharmacology Approach

Ochieng

Kusuma

Rafi

et al. 2017

Int J Pharm Pharm Sci

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Objective: The aim of this research was to investigate action mechanism of Indonesia herbal decoctions in the treatment of Type 2 Diabetes (T2D) using network pharmacology approaches.Methods: Drug target profile analysis via Markov clustering was performed to identify the potent antidiabetic ingredients in the four herbs. Network target base identification of multicomponent synergy was applied to predict the ingredients synergetic effect. The multi-level and integrated target networks were contracted to identify the herbs major ingredients and their presumed targets. Further enrichment analysis and molecular docking were performed to validate network targets.Results: 278 ingredients from the four herbs were linked to antidiabetic drugs with an overall clustering success rate of 98.58% and 5 ingredient pairs had significant synergetic effects. Enrichment analysis demonstrates herbs candidate presumed targets were frequently involved in the significant biological process and pathways associated with progression of Type 2 diabetes (T2D) diseases. Finally, molecular docking validation revealed there was high binding site similarity between momordicoside F2 (78%), beta-sitosterol (67%) and cis-N-Feruloyltyramine (67%) with miglitol drug. In addition, the four ligands presented the higher binding affinity to Maltase-glucoamylase (MGA) receptor an enzyme responsible for the digestion of dietary starch to glucose.Conclusion: This study revealed the pharmacological mechanism of action of Indonesia herbal decoctions in the treatment of Type 2 diabetes. The herbs major presumed target played a significant biological role in the progression of Type 2 diabetes (T2D) while major herbal ingredients indicates the potential of curing Type 2 diabetes by inhibiting Maltase-glucoamylase (MGA) activity.

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Integrative analysis of human protein, function and disease networks

Cited by 34 publications

References 61 publications

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Statistical physics of medical diagnostics: Study of a probabilistic model

Deciphering the Action Mechanism of Indonesia Herbal Decoction in the Treatment of Type Ii Diabetes Using a Network Pharmacology Approach

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