Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

Predicted Arabidopsis Interactome Resource-A Network Modeling Method of Integration and Analysis for the Omics Data

Authors
Heng Yao, Xin Chen, Xiaoxuan Wang
Corresponding Author
Heng Yao
Available Online November 2017.
DOI
10.2991/amms-17.2017.25How to use a DOI?
Keywords
omics annotation; arabidopsis; functional interaction network; analysis method
Abstract

The Predicted Arabidopsis Interactome Resource (PAIR) is an online database of the functional interactions between Arabidopsis genes. PAIR is inferred by integrating six types of evidence each of which suggests a different aspect of functional associations between Arabidopsis genes and therefore enables extended analysis on the potential functional impacts of the observed omics changes at the physiological level.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
Series
Advances in Intelligent Systems Research
Publication Date
November 2017
ISBN
978-94-6252-433-0
ISSN
1951-6851
DOI
10.2991/amms-17.2017.25How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Heng Yao
AU  - Xin Chen
AU  - Xiaoxuan Wang
PY  - 2017/11
DA  - 2017/11
TI  - Predicted Arabidopsis Interactome Resource-A Network Modeling Method of Integration and Analysis for the Omics Data
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 110
EP  - 113
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.25
DO  - 10.2991/amms-17.2017.25
ID  - Yao2017/11
ER  -