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INFER: Interactive Timing Profiles based on Bayesian Networks

Michael Zolda, Marc Herbstritt
2008
We propose an approach for timing analysis of software-based embedded computer systems that builds on the established probabilistic framework of Bayesian networks.  ...  seen as an interactive timing profile.  ...  Related Work Lemeire and Dirkx [7] present an approach for performance analysis of concurrent systems that is based on Bayesian networks.  ... 
doi:10.4230/oasics.wcet.2008.1669 fatcat:7cjsbmv5abfqnpuatyiyexs4ia

Inferring regulatory networks

Huai Li
2008 Frontiers in Bioscience  
Computational approaches for inferring gene connectivity 4.1. ODE-based models 4.2. Bayesian networks 4.3. Coexpression networks 4.4. Probabilistic boolean networks 4.5.  ...  This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other data sources, including transcriptional module identification, network  ...  Zhiping Gu for their comments on this review, the Intramural Research Program, National Institute on Aging, NIH and the National Institutes of Health (under Grants CA109872, EB000830) for generous support  ... 
doi:10.2741/2677 pmid:17981545 fatcat:tywmilxq4bc6vlnf7fr6wwfz3i

Inferring cellular networks – a review

Florian Markowetz, Rainer Spang
2007 BMC Bioinformatics  
The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks.  ...  The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.  ...  Nested effects models A key obstacle to inferring genetic networks from perturbation screens is that phenotypic profiles generally offer only indirect information on how genes interact.  ... 
doi:10.1186/1471-2105-8-s6-s5 pmid:17903286 pmcid:PMC1995541 fatcat:3cqptcs6ord5zkrhnyuvhu7svq

Bayesian Inference of Gene Regulatory Network [chapter]

Xi Chen, Jianhua Xuan
2020 Bayesian Inference on Complicated Data  
Bayesian inference (or integration) has been successfully applied to inferring GRNs.  ...  In this chapter, we introduced GRN modeling using hierarchical Bayesian network and then used Gibbs sampling to identify network variables.  ...  Acknowledgements Funding for open access charge: Virginia Tech's Open Access Subvention Found (VT OASF). 13 Bayesian Inference of Gene Regulatory Network DOI: http://dx.doi.org /10.5772/intechopen.88799  ... 
doi:10.5772/intechopen.88799 fatcat:c64rtict35aj5o376amu3l3wyy

How to infer gene networks from expression profiles

Mukesh Bansal, Vincenzo Belcastro, Alberto Ambesi-Impiombato, Diego di Bernardo
2007 Molecular Systems Biology  
Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis.  ...  We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements  ...  Banjo is based on Bayesian networks formalism and implements both Bayesian and Dynamic Bayesian networks.  ... 
doi:10.1038/msb4100120 pmid:17299415 pmcid:PMC1828749 fatcat:p4suf5g3t5eilevewhkiikimhq

How to infer gene networks from expression profiles

Mukesh Bansal, Vincenzo Belcastro, Alberto Ambesi-Impiombato, Diego di Bernardo
2007 Molecular Systems Biology  
Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis.  ...  We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements  ...  Banjo is based on Bayesian networks formalism and implements both Bayesian and Dynamic Bayesian networks.  ... 
doi:10.1038/msb4100158 fatcat:zwomuy5v3bfuvpgxcj2hskkiyq

Inferring subnetworks from perturbed expression profiles

D. Pe'er, A. Regev, G. Elidan, N. Friedman
2001 Bioinformatics  
In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework.  ...  Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations.  ...  Acknowledgments The authors are grateful to Michal Chur, Rani Nelken, Matan Ninio, Itsik Pe'er and Eran Segal for comments on drafts of this paper and useful discussions.  ... 
doi:10.1093/bioinformatics/17.suppl_1.s215 pmid:11473012 fatcat:5tpwpjijwvaifehw5i67afy3ae

SIRENE: supervised inference of regulatory networks

F. Mordelet, J.-P. Vert
2008 Bioinformatics  
We test it on a benchmark experiment aimed at predicting regulations in E. coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.  ...  Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only  ...  For each TF, we estimate a local model to discriminate, based on their expression profiles, the genes regulated by the TF from others genes.  ... 
doi:10.1093/bioinformatics/btn273 pmid:18689844 fatcat:q67lsf2nx5hcli6tgbxzrcctjq

A Network-Based, Multidisciplinary Approach to Intention Inference

Lihi Idan
2022 CHI Conference on Human Factors in Computing Systems Extended Abstracts  
CCS CONCEPTS • Human-centered computing → Social media; • Computing methodologies → Bayesian network models.  ...  Using Bayesian-Networks, we model a behavioral intention using a set of causes that influence the intention's formation, a set of effects that are caused by the intention, and various dependency relations  ...  in inferring intentions of social-network users using public data extracted from their social-network profiles.  ... 
doi:10.1145/3491101.3519754 fatcat:zql23m2jjrdlhpcnkv65cs7jv4

Statistical inference of regulatory networks for circadian regulation

Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk
2014 Statistical Applications in Genetics and Molecular Biology  
are described by a Markov jump process based on Michaelis-Menten kinetics.  ...  In addition, gene expression and protein concentration time series are simulated from a recently published regulatory network of the circadian clock in A. thaliana, in which protein and gene interactions  ...  Acknowledgments: The work described in the present article is part of the TiMet project on linking the circadian clock to metabolism in plants.  ... 
doi:10.1515/sagmb-2013-0051 pmid:24864301 fatcat:6po5swfc25a6lisphtyrtyamwy

Causal Network Inference for Neural Ensemble Activity

Rong Chen
2021 Neuroinformatics  
A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series.  ...  CAIM combines neural recording, Bayesian network modeling, and neuron clustering.  ...  based on dynamic Bayesian networks (DBNs) (Eldawlatly et al. 2010) .  ... 
doi:10.1007/s12021-020-09505-4 pmid:33393054 fatcat:6tstmuzygfgodeoniwcjcui2di

Inferring Beliefs from Actions

Itai Arieli, Manuel Mueller-Frank
2013 Social Science Research Network  
On the basis of the single-agent belief revelation result we establish information aggregation results in the framework of repeated interaction in social networks and in the sequential social learning  ...  model. * The authors wish to express their gratitude to Alan Beggs for valuable comments on an earlier version.  ...  We focus on the Perfect Bayesian equilibria of the network interaction game therefore allowing for fully strategic agents.  ... 
doi:10.2139/ssrn.2208083 fatcat:7g54wsrxdres3j3quq6gwn5bam

Active Inferants: An Active Inference Framework for Ant Colony Behavior

Daniel Ari Friedman, Alec Tschantz, Maxwell J. D. Ramstead, Karl Friston, Axel Constant
2021 Frontiers in Behavioral Neuroscience  
In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments.  ...  Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology.  ...  Variational inference is a method for approximate Bayesian inference and depends on two distributions: the variational distribution and the generative model.  ... 
doi:10.3389/fnbeh.2021.647732 pmid:34248515 pmcid:PMC8264549 fatcat:nvurzinpybc2hftnfah2atu4w4

Pathway-based Bayesian inference of drug–disease interactions

Naruemon Pratanwanich, Pietro Lió
2014 Molecular Biosystems  
Additionally, we demonstrate four case studies illustrating that the between-pathway network enhances the performance of pathway identification and provides insights into disease comorbidity, drug repositioning  ...  Firstly, we developed a Bayesian matrix factorisation of gene expression data together with known gene-pathway memberships to identify pathways perturbed by drugs.  ...  Acknowledgements We thank Dr Viet Anh Nguyen for a productive discussion on Bayesian approaches. We thank FP7-ICT Mission T2D for funding. NP acknowledges the Royal Thai Government Scholarship.  ... 
doi:10.1039/c4mb00014e pmid:24695945 fatcat:mr3bnifzlrcqtlzjugh7ce7tyy

Mood Inference Machine: Framework to Infer Affective Phenomena in ROODA Virtual Learning Environment

Magalí Teresinha Longhi, Patricia Alejandra Behar, Magda Bercht
2012 International Journal of Advanced Corporate Learning  
In the inference machine, such variables are treated under probability reasoning, more precisely by bayesian networks.  ...  Index Terms-Affective modeling, Bayesian networks, virtual learning environments. 14  ...  She was responsible by application and analysis of IFP instrument on the participants of the case study.  ... 
doi:10.3991/ijac.v5i1.1740 fatcat:czqwnxzkxjcelas7yr5733ppsm
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