Maya: a multi-paradigm network modeling framework for emulating distributed applications

2004 ◽  
Author(s):  
J. Zhou ◽  
Z. Ji ◽  
M. Takai ◽  
R. Bagrodia
Keyword(s):  
Network Modeling ◽  
Distributed Applications ◽  
Modeling Framework

scholarly journals Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

2019 ◽  
Author(s):  
Rupam Bhattacharyya ◽  
Min Jin Ha ◽  
Qingzhi Liu ◽  
Rehan Akbani ◽  
Han Liang ◽  
...  
Keyword(s):  
Cancer Patient ◽  
Head And Neck ◽  
Cell Line ◽  
Cell Lines ◽  
Network Modeling ◽  
Model Systems ◽  
Medical Decision ◽  
Modeling Framework ◽  
Multiple Drugs ◽  
Pan Cancer

ABSTRACTPurposePersonalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.Datasets and methodsWe developed TransPRECISE, a multi-scale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intra-pathway activities, globally assess cell lines as representative models for patients and develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (>7700) from The Cancer Proteome Atlas across ≥30 tumor types and a set of 640 cancer cell lines from the M.D. Anderson Cell Lines Project spanning16 lineages, and ≥250 cell lines’ response to >400 drugs.ResultsTransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: head and neck patient tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines, and had different prognostic patterns (456 vs. 654 days of median overall survival; P=0.02). The TransPRECISE-based sample-specific pathway scores achieved high predictive accuracy for drug sensitivities in cell lines across multiple drugs (median AUC >0.8).ConclusionOur study provides a generalizable analytical framework to assess the translational potential of preclinical model systems and guide pathway-based personalized medical decision-making, integrating genomic and molecular data across model systems.

scholarly journals Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jia-Qi Liu ◽  
Yun-Wen Feng ◽  
Cheng Lu ◽  
Wei-Huang Pan ◽  
Da Teng
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Short Form ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Time Varying ◽  
Rotor Vibration ◽  
Modeling Framework ◽  
Highly Nonlinear ◽  
Vibration Reliability

In order to improve the accuracy and calculation efficiency of aeroengine rotor vibration reliability analysis, a time-varying rotor vibration reliability analysis method under the aeroengine operating state is proposed. Aiming at the highly nonlinear and strong coupling of factors affecting the reliability of aeroengine rotor vibration, an intelligent neural network modeling framework (short form-INNMF) is proposed. The proposed method is based on DEA, with QAR information as the analysis data, and four factors including engine working state, fuel/oil working state, aircraft flight state, and external conditions are considered to analyse the rotor vibration reliability. INNMF is based on the artificial neural network (ANN) algorithm through improved particle swarm optimization (PSO) algorithm and Bayesian Regularization (BR) optimization. Through the analysis of the rotor vibration reliability of the B737-800 aircraft during a flight mission from Beijing to Urumqi, the time-varying rotor vibration reliability was obtained, which verified the effectiveness and feasibility of the method. The comparison of INNMF, random forest (RF), and ANN shows that INNMF improves analysis accuracy and calculation efficiency. The proposed method and framework can provide useful references for aeroengine rotor vibration analysis, special treatment, maintenance, and design.

scholarly journals Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

2021 ◽  
Author(s):  
Maalavika Pillai ◽  
Mohit Kumar Jolly
Keyword(s):  
Network Modeling ◽  
Expression Patterns ◽  
Therapeutic Strategies ◽  
Phenotypic Switching ◽  
Modeling Framework ◽  
Master Regulators ◽  
Tumor Relapse ◽  
Braf Inhibition ◽  
Dynamical Simulations ◽  
Novel Therapeutic Strategies

AbstractPhenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple “attractor” states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different “attractors” (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

scholarly journals The Impacts of Surface Microchannels on the Transport Properties of Porous Fibrous Media Using Stochastic Pore Network Modeling

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7546
Author(s):  
Xiang Huang ◽  
Wei Zhou ◽  
Daxiang Deng ◽  
Bin Liu ◽  
Kaiyong Jiang
Keyword(s):  
Transport Properties ◽  
Network Modeling ◽  
Catalyst Support ◽  
Sampling Strategy ◽  
Pore Network ◽  
Propagation Path ◽  
Original Network ◽  
Fibrous Media ◽  
Modeling Framework ◽  
Pore Network Modeling

A stochastic pore network modeling method with tailored structures is proposed to investigate the impacts of surface microchannels on the transport properties of porous fibrous media. Firstly, we simplify the original pore network extracted from the 3D images. Secondly, a repeat sampling strategy is applied during the stochastic modeling of the porous structure at the macroscale while honoring the structural property of the original network. Thirdly, the microchannel is added as a spherical chain and replaces the overlapped elements of the original network. Finally, we verify our model via a comparison of the structure and flow properties. The results show that the microchannel increases the permeability of flow both in the directions parallel and vertical to the microchannel direction. The microchannel plays as the highway for the pass of reactants while the rest of the smaller pore size provides higher resistance for better catalyst support, and the propagation path in the network with microchannels is more even and predictable. This work indicates that our modeling framework is a promising methodology for the design optimization of cross-scale porous structures.

scholarly journals A Quantitative View of the Structure of Institutional Scientific Collaborations Using the Example of Berlin

2021 ◽  
pp. 1-24
Author(s):  
Aliakbar Akbaritabar
Keyword(s):  
Peer Review ◽  
Regional Cooperation ◽  
Network Modeling ◽  
Health Sciences ◽  
Bipartite Network ◽  
Team Science ◽  
Name Disambiguation ◽  
Modeling Framework ◽  
History Of ◽  
Scientific Collaborations

Abstract This paper examines the structure of scientific collaborations in Berlin as a specific case with a unique history of division and reunification. It aims to identify strategic organizational coalitions in a context with high sectoral diversity. We use publications data with at least one organization located in Berlin from 1996-2017 and their collaborators worldwide. We further investigate four members of the Berlin University Alliance (BUA), as a formerly established coalition in the region, through their self-represented research profiles compared with empirical results. Using a bipartite network modeling framework, we move beyond the uncontested trend towards team science and increasing internationalization. Our results show that BUA members shape the structure of scientific collaborations in the region. However, they are not collaborating cohesively in all fields and there are many smaller scientific actors involved in more internationalized collaborations in the region. Larger divides exist in some fields. Only Medical and Health Sciences have cohesive intraregional collaborations which signals the success of the regional cooperation established in 2003. We explain possible underlying factors shaping the intraregional groupings and potential implications for regions worldwide. A major methodological contribution of this paper is evaluating coverage and accuracy of different organization name disambiguation techniques. Peer Review https://publons.com/publon/10.1162/qss_a_00131

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