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miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

MiniTUBA currently contains both synthetic data and real biomedical research data. Any user can login using our demonstration account and can use synthetic data to test the functions available in the current system. Feedback is welcome. Thanks. To cite miniTUBA, please reference the following publication:. Zuoshuang Xiang, Rebecca M. Minter, Xiaoming Bi, Peter Woolf, Yongqun He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

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miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis | minituba.org Reviews

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MiniTUBA currently contains both synthetic data and real biomedical research data. Any user can login using our demonstration account and can use synthetic data to test the functions available in the current system. Feedback is welcome. Thanks. To cite miniTUBA, please reference the following publication:. Zuoshuang Xiang, Rebecca M. Minter, Xiaoming Bi, Peter Woolf, Yongqun He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

<META> KEYWORDS

1 sandbox demo

2 research projects

3 tutorial

4 documentation

5 faqs

6 welcome to minituba

7 publications

8 bioinformatics

9 minituba applications

10 brucella suis

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sandbox demo,research projects,tutorial,documentation,faqs,welcome to minituba,publications,bioinformatics,minituba applications,brucella suis,infection and immunity,register,acknowledgements,links,disclaimer

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miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis | minituba.org Reviews

https://minituba.org

MiniTUBA currently contains both synthetic data and real biomedical research data. Any user can login using our demonstration account and can use synthetic data to test the functions available in the current system. Feedback is welcome. Thanks. To cite miniTUBA, please reference the following publication:. Zuoshuang Xiang, Rebecca M. Minter, Xiaoming Bi, Peter Woolf, Yongqun He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

INTERNAL PAGES

minituba.org

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miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

http://www.minituba.org/links.php

Tools being used in miniTUBA:. Banjo (Bayesian Network Inference with Java Objects): http:/ www.cs.duke.edu/ amink/software/banjo/. Graphviz: http:/ www.graphviz.org/. LiveGraphics3D: http:/ wwwvis.informatik.uni-stuttgart.de/ kraus/LiveGraphics3D/index.html. The R Project for Statistical Computing: http:/ www.r-project.org/. ImageMagick: http:/ www.imagemagick.org/script/index.php. Bayesian network From Wikipedia: http:/ en.wikipedia.org/wiki/Bayesian network.

2

miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

http://www.minituba.org/acknowledgements.php

Acknowledgements / Grant Support. Support for the miniTUBA project is derived from the departments of Laboratory Animal Medicine (Dr. Yongqun He), Chemical Engineering (Dr. Peter Woolf), Surgery (Dr. Rebecca M. Minter), Microbiology and Immunology (Dr. He), and Bioinformatics Program (Drs. He and Woolf) at the University of Michigan, and the following grant support:. University of Michigan Institutional Fund. National Institute of Allergy and Infectious Diseases (NIAID, NIH). NIH NIGMS K08 GM074678-01A1.

3

miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

http://www.minituba.org/index.php

MiniTUBA currently contains both synthetic data and real biomedical research data. Any user can login using our demonstration account and can use synthetic data to test the functions available in the current system. Feedback is welcome. Thanks. To cite miniTUBA, please reference the following publication:. Zuoshuang Xiang, Rebecca M. Minter, Xiaoming Bi, Peter Woolf, Yongqun He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

4

miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

http://www.minituba.org/faqs.php

1 What is miniTUBA? MiniTUBA stands for the Medical Inference by Network Integration of Temporal Data using Bayesian Analysis. It is a web-based dynamic Bayesian network analysis system used to simulate biomedical networks using temporal data. The results from these analyses can then be utilized to identify causal or potentially causal relationships between data elements. The DBN analysis pipeline developed by miniTUBA is widely applicable and can be useful for generating networks using not o...

5

miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

http://www.minituba.org/projects/index.php

We have hosted a number of research projects (public):. Immunomodulation of Sepsis in Biliary Obstruction. By Rebecca M. Minter at University of Michigan. Dexamethasone Regulation Ongoing Inflammation. By Devin Horton at Boston University. Genetic network in Brucella suis infected macrophages. By Oliver He at University of Michigan. Laparoscopioc Adjustable Gastric Banding for the Treatment of Morbid Obesity in Adolescents. By Evan P. Nadler MD at NYU School of Medicine. Bioinformatics 800 Di Ma. BN of T...

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hegroup.org

He Group Web Links

http://www.hegroup.org/links.html

Links to Dr. He's pages:. In the Unit for Laboratory Animal Medicine (ULAM). In the Department of Microbiology and Immunology. Dr He Research Laboratory. University of Michigan Links:. University of Michigan Medical School. Unit for Laboratory Animal Medicine (ULAM). Department of Microbiology and Immunology. Center for Computational Biology and Medicine. Including UM Taubman Library. Http:/ www.ucsf.edu/news/2014/02/111956/science-policy-team-wins-national-video-content. Ann Arbor, MI 48109.

hegroup.org

Welcome to He Group Webpage

http://www.hegroup.org/index.php

Welcome to hegroup.org! Welcome to the home page of Dr. Yongqun Oliver He. S laboratory research group at the University of Michigan! We do both dry-lab (bioinformatics lab) and wet-lab (microbiology and immunology lab) biomedical research. Our primary bioinformatics research has many topics: (1) Ontology development. Dr He has initiated and led the development of several community-based ontologies, including Vaccine Ontology. VO), Ontology of Adverse Events. OAE), Ontology of Genes and Genomes. Vaccine ...

hegroup.org

He Group Bioinformatics Projects

http://www.hegroup.org/projects.html

We do both bioinformatics (dry-lab). A He Group bioinformatics projects:. Our bioinformatics research focus is to develop ontologies, ontology-based tools, databases, and analysis systems for addressign various biomedical questions. Our primary bioinformatics research has many topics:. Dr He has initiated and led the development of the community-based Vaccine Ontology. VO) and Ontology of Adverse Events. We have developed many ontology tools. With a focus on ontology-based literature mining approaches.

hegroup.org

He Group: FAQs

http://www.hegroup.org/faqs.php

1 Who is He Group? He Group is the research group in Dr. Yongqun Oliver He's laboratory located in the University of Michigan Medical School. He Group includes integrated wet-lab group and bioinformatics group. Our primary research interests are pathogen-host interactions ( PHI. And vaccine research and development (R&D). Please look our Projects. 2 What software programs developed in He Group can be used to study infectious diseases? Genomics data integration and literature annotation, and study of.

hegroup.org

The OneNet Theory of Life

http://www.hegroup.org/onenettheory.html

The OneNet Theory of Life. The author first proposed the One Network (abbreviated as OneNet) Theory of Life (for one organism) in a peer-reviewed article published in the journal. Expert Reviews in Vaccines. In June 2014 ( He, 2014. And the author further extended the theory in another article in. In 2016 ( He, 2016. The OneNet theory theory is targeted to explain and seek the deep mechanisms of various phenotypes and phenomena of the life of an organism (for example, a single cell bacterium like. An org...

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miniTUBA: Medical Inference by Network Integration of Temporal Data Using Bayesian Analysis

MiniTUBA currently contains both synthetic data and real biomedical research data. Any user can login using our demonstration account and can use synthetic data to test the functions available in the current system. Feedback is welcome. Thanks. To cite miniTUBA, please reference the following publication:. Zuoshuang Xiang, Rebecca M. Minter, Xiaoming Bi, Peter Woolf, Yongqun He. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis.

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