scholarly journals A semi-parametric statistical test to compare complex networks

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Andre Fujita ◽  
Eduardo Silva Lira ◽  
Suzana de Siqueira Santos ◽  
Silvia Yumi Bando ◽  
Gabriela Eleuterio Soares ◽  
...  
Keyword(s):  
Complex Networks ◽  
Random Process ◽  
Real World ◽  
Regulatory Networks ◽  
Generative Models ◽  
Real World Data ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Functional Brain Networks ◽  
Parametric Statistical Test

Abstract The modelling of real-world data as complex networks is ubiquitous in several scientific fields, for example, in molecular biology, we study gene regulatory networks and protein–protein interaction (PPI)_networks; in neuroscience, we study functional brain networks; and in social science, we analyse social networks. In contrast to theoretical graphs, real-world networks are better modelled as realizations of a random process. Therefore, analyses using methods based on deterministic graphs may be inappropriate. For example, verifying the isomorphism between two graphs is of limited use to decide whether two (or more) real-world networks are generated from the same random process. To overcome this problem, in this article, we introduce a semi-parametric approach similar to the analysis of variance to test the equality of generative models of two or more complex networks. We measure the performance of the proposed statistic using Monte Carlo simulations and illustrate its usefulness by comparing PPI networks of six enteric pathogens.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

scholarly journals Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment

2021 ◽  
Vol 11 ◽  
Author(s):  
Yeltai Nurzat ◽  
Weijie Su ◽  
Peiru Min ◽  
Ke Li ◽  
Heng Xu ◽  
...  
Keyword(s):  
T Cells ◽  
Cutaneous Melanoma ◽  
Regulatory Networks ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Expression Levels ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Go Enrichment ◽  
Underlying Mechanisms

The roles of different integrin alpha/beta (ITGA/ITGB) subunits in skin cutaneous melanoma (SKCM) and their underlying mechanisms of action remain unclear. Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, and Webgestalt analysis tools were used. The expression levels of ITGA3, ITGA4, ITGA6, ITGA10, ITGB1, ITGB2, ITGB3, ITGB4, and ITGB7 were significantly increased in SKCM tissues. The expression levels of ITGA1, ITGA4, ITGA5, ITGA8, ITGA9, ITGA10, ITGB1, ITGB2, ITGB3, ITGB5, ITGB6 and ITGB7 were closely associated with SKCM metastasis. The expression levels of ITGA1, ITGA4, ITGB1, ITGB2, ITGB6, and ITGB7 were closely associated with the pathological stage of SKCM. The expression levels of ITGA6 and ITGB7 were closely associated with disease-free survival time in SKCM, and the expression levels of ITGA6, ITGA10, ITGB2, ITGB3, ITGB6, ITGB7, and ITGB8 were markedly associated with overall survival in SKCM. We also found significant correlations between the expression of integrin subunits and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells). Finally, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed, and protein-protein interaction (PPI) networks were constructed. We have identified abnormally-expressed genes and gene regulatory networks associated with SKCM, improving understanding of the underlying pathogenesis of SKCM.

scholarly journals Uncovering structural, evolutionary and spreading-phenomena characteristics of complex networks and systems using statistical mechanics methods on real-world data

2020 ◽  
Author(s):  
Μαρία Τσουχνικά
Keyword(s):  
Statistical Mechanics ◽  
Complex Networks ◽  
Real World ◽  
Real World Data ◽  
World Data

Οι ερευνητικές και οικονομικές διεργασίες, καθώς και η ανάπτυξη καινοτομιών, αποτελούν τρία αλληλένδετα κοινωνικά πολύπλοκα συστήματα, τα οποία περιλαμβάνουν πολυποίκιλες ανθρώπινες σχέσεις. Τα κοινωνικά αυτά συστήματα βρίσκονται σε μία κατάσταση συνεχούς αλληλεπίδρασης και συνδιαμόρφωσης με την κοινωνία. Αφενός, οι διεργασίες αυτές ορίζονται εν πολλοίς από τις επικρατούσες κοινωνικές συνθήκες και τους κανονισμούς που οι κοινωνίες επιβάλλουν, αφετέρου, οι διεργασίες αυτές με τη σειρά τους επηρεάζουν και διαπλάθουν την καθημερινότητά μας και τις συνθήκες διαβίωσης. Κατά συνέπεια, το ενδιαφέρον μας γύρω από τη βελτιστοποίηση αυτών των διεργασιών είναι έκδηλο και αέναο. Η κατανόηση και ο εντοπισμός προβλημάτων, αλλά και επιτυχιών, των διεργασιών αυτών αποτελεί τη βάση της διαδικασίας βελτιστοποίησής τους. Στη διατριβή αυτή, αναλύονται τρία τέτοια αντιπροσωπευτικά συστήματα, ένα για κάθε τύπο αυτών των διεργασιών, μελετόντας τα αντίστοιχα παραγόμενα δίκτυα, χρησιμοποιώντας τεχνικές θεωρίας γράφων (δικτύων). Συγκεκριμένα, εφαρμόζονται μεθοδολογίες ανάλυσης της δομής και της εξέλιξης δικτύων, αλλά και φαινομένων διάδοσης πάνω σε αυτά. Στο κεφάλαιο 2 της διατριβής, εξετάζεται η δομή ενός δικτύου που αφορά στην έρευνα· το δίκτυο των ερευνητικών συνεργασιών που αποτυπώνονται στις ερευνητικές προτάσεις που έχουν υποβληθεί στο 7ο Πρόγραμμα – Πλαίσιο (7ο ΠΠ), Ευρωπαϊκό πρόγραμμα χρηματοδοτησης της έρευνας. Στο κεφάλαιο 3, η διατριβή πραγματεύεται ένα σύστημα οικονομικών διεργασιών. Ειδικότερα, μελετώνται οι συνθήκες διάδοσης μίας οικονομικής κρίσης στο παγκόσμιο δίκτυο των πόλεων και των μεταξύ τους οικονομικών αλληλεπιδράσεων, οι οποίες αλληλεπιδράσεις αντανακλώνται στα δεδομένα ιδιοκτησιακών σχέσεων εταιρειών που βρίσκονται εντός των πόλεων του δικτύου. Τέλος, στο κεφάλαιο 4, εξετάζεται ένα σύστημα διεργασιών ανάπτυξης καινοτομιών και συγκεκριμένα μελετώνται τα χαρακτηριστικά της χρονικής εξέλιξης του δικτύου των συνεργασιών που αναπτύσονται στα πλαίσια δημιουργίας καινοτομιών, οι οποίες συνεργασίες αντικατοπτρίζονται στα τα δεδομένα των πατεντών που έχουν υποβληθεί στο Ευρωπαϊκό Γραφείο Διπλωμάτων Ευρεσιτεχνίας (ΕΓΔΕ).

scholarly journals Identification of key candidate genes and pathways in axial Spondyloarthritis through integrated bioinformatics analysis

2020 ◽  
Author(s):  
Zhen-zhen Zhang ◽  
Jing Zeng ◽  
Hai-hong Li ◽  
Yu-cong Zou ◽  
Shuang Liang ◽  
...  
Keyword(s):  
Immunohistochemical Staining ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Axial Spondyloarthritis ◽  
Kegg Pathway ◽  
Transcriptional Regulatory Network ◽  
Rt Pcr ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Pathway Analyses

AbstractBackgroundRadiographic axial Spondyloarthritis (r-axSpA) is the prototypic form of seronegative spondyloarthritis (SpA). In the present study, we evaluated the key genes related with r-axSpA, and then elucidated the possible molecular mechanisms of r-axSpA.Material/MethodsThe gene expression GSE13782 was downloaded from the GEO database contained five proteoglycan-induced spondylitis mice and three naïve controls. The differentially expressed genes (DEGs) were identified with the Bioconductor affy package in R. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were built with the DAVID program followed by construction of a protein-protein interaction (PPI) network performed with Cytoscape. WebGestalt was performed to construct transcriptional regulatory network and microRNAs-target regulatory networks. RT-PCR and immunohistochemical staining were performed to testify the expression of hub genes, transcription factors (TFs) and microRNAs.ResultsA total of 230 DEGs were identified. PPI networks were constructed by mapping DEGs into STRING, in which 20 hub proteins were identified. KEGG pathway analyses revealed that the chemokine, NOD-like receptor, IL-17, and TNF signalling pathways were altered. GO analyses revealed that DEGs were extensively involved in the regulation of cytokine production, the immune response, the external side of the plasma membrane, and G-protein coupled chemoattractant receptor activity. The results of RT-PCR and immunohistochemical staining demonstrated that the expression of DEGs, TFs and microRNAs in our experiment were basically consistent with the predictions.ConclusionsThe results of this study offer insight into the pathomechanisms of r-axSpA and provide potential research directions.

scholarly journals An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Amina Noor ◽  
Erchin Serpedin ◽  
Mohamed Nounou ◽  
Hazem Nounou ◽  
Nady Mohamed ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Gene Regulatory Networks ◽  
Protein Interaction ◽  
Regulatory Networks ◽  
Space Representation ◽  
Graphical Modeling ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Modeling Techniques ◽  
Gene Regulatory

The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.

scholarly journals Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Christina Y. Yu ◽  
Antonina Mitrofanova
Keyword(s):  
Regulatory Networks ◽  
Biomarker Discovery ◽  
Disease Classification ◽  
Therapeutic Targeting ◽  
Future Directions ◽  
Single Cell Sequencing ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Precision Therapeutics

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein–protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

scholarly journals Link Prediction through Deep Generative Model

2018 ◽  
Author(s):  
Xu-Wen Wang ◽  
Yize Chen ◽  
Yang-Yu Liu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Method ◽  
Generative Models ◽  
Generative Model ◽  
Superior Performance ◽  
Prediction Problem ◽  
Structural Patterns ◽  
Domain Specific

AbstractInferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine1–3, e-commerce4, social media5 and criminal intelligence6. Numerous methods have been proposed to solve the link prediction problem7–9. Yet, many of these existing methods are designed for undirected networks only. Moreover, most methods are based on domain-specific heuristics10, and hence their performances differ greatly for networks from different domains. Here we developed a new link prediction method based on deep generative models11 in machine learning. This method does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities12. Conceptually, taking into account structural patterns at different scales all together should outperform any domain-specific heuristics that typically focus on structural patterns at a particular scale. Indeed, when applied to various real-world networks from different domains13–17, our method shows overall superior performance against existing methods. Moreover, it can be easily parallelized by splitting a large network into several small subnetworks and then perform link prediction for each subnetwork in parallel. Our results imply that deep learning techniques can be effectively applied to complex networks and solve the classical link prediction problem with robust and superior performance.SummaryWe propose a new link prediction method based on deep generative models.

scholarly journals Deep learning based network similarity for model selection

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Kushal Veer Singh ◽  
Ajay Kumar Verma ◽  
Lovekesh Vig
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Complex Networks ◽  
Real World ◽  
Network Architecture ◽  
Large Scale ◽  
Generative Models ◽  
Generative Model ◽  
Data Set ◽  
Feed Forward Network

Capturing data in the form of networks is becoming an increasingly popular approach for modeling, analyzing and visualising complex phenomena, to understand the important properties of the underlying complex processes. Access to many large-scale network datasets is restricted due to the privacy and security concerns. Also for several applications (such as functional connectivity networks), generating large scale real data is expensive. For these reasons, there is a growing need for advanced mathematical and statistical models (also called generative models) that can account for the structure of these large-scale networks, without having to materialize them in the real world. The objective is to provide a comprehensible description of the network properties and to be able to infer previously unobserved properties. Various models have been developed by researchers, which generate synthetic networks that adhere to the structural properties of real networks. However, the selection of the appropriate generative model for a given real-world network remains an important challenge. In this paper, we investigate this problem and provide a novel technique (named as TripletFit) for model selection (or network classification) and estimation of structural similarities of the complex networks. The goal of network model selection is to select a generative model that is able to generate a structurally similar synthetic network for a given real-world (target) network. We consider six outstanding generative models as the candidate models. The existing model selection methods mostly suffer from sensitivity to network perturbations, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad array of network features, with the aim of representing different structural aspects of the network and employed deep learning techniques such as deep triplet network architecture and simple feed-forward network for model selection and estimation of structural similarities of the complex networks. Our proposed method, outperforms existing methods with respect to accuracy, noise-tolerance, and size independence on a number of gold standard data set used in previous studies.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data

Safety and efficacy of direct acting oral anticoagulants and vitamin K antagonists in nonvalvular atrial fibrillation – a network meta-analysis of real-world data

VASA ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 134-147 ◽  
Author(s):  
Mirko Hirschl ◽  
Michael Kundi
Keyword(s):  
Real World ◽  
Meta Analysis ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Efficacy And Safety ◽  
Nonvalvular Atrial Fibrillation ◽  
Real World Data ◽  
Hazard Ratios ◽  
Direct Acting ◽  
Event Rates

Abstract. Background: In randomized controlled trials (RCTs) direct acting oral anticoagulants (DOACs) showed a superior risk-benefit profile in comparison to vitamin K antagonists (VKAs) for patients with nonvalvular atrial fibrillation. Patients enrolled in such studies do not necessarily reflect the whole target population treated in real-world practice. Materials and methods: By a systematic literature search, 88 studies including 3,351,628 patients providing over 2.9 million patient-years of follow-up were identified. Hazard ratios and event-rates for the main efficacy and safety outcomes were extracted and the results for DOACs and VKAs combined by network meta-analysis. In addition, meta-regression was performed to identify factors responsible for heterogeneity across studies. Results: For stroke and systemic embolism as well as for major bleeding and intracranial bleeding real-world studies gave virtually the same result as RCTs with higher efficacy and lower major bleeding risk (for dabigatran and apixaban) and lower risk of intracranial bleeding (all DOACs) compared to VKAs. Results for gastrointestinal bleeding were consistently better for DOACs and hazard ratios of myocardial infarction were significantly lower in real-world for dabigatran and apixaban compared to RCTs. By a ranking analysis we found that apixaban is the safest anticoagulant drug, while rivaroxaban closely followed by dabigatran are the most efficacious. Risk of bias and heterogeneity was assessed and had little impact on the overall results. Analysis of effect modification could guide the clinical decision as no single DOAC was superior/inferior to the others under all conditions. Conclusions: DOACs were at least as efficacious as VKAs. In terms of safety endpoints, DOACs performed better under real-world conditions than in RCTs. The current real-world data showed that differences in efficacy and safety, despite generally low event rates, exist between DOACs. Knowledge about these differences in performance can contribute to a more personalized medicine.

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