A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis

Arinze Akutekwe; Huseyin Seker

doi:10.1109/EMBC.2014.6943713

A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis

Arinze Akutekwe
, Huseyin Seker^*

^*Corresponding author for this work

De Montfort University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (SciVal)

Original language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	804-807
Number of pages	4
ISBN (Electronic)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6943713
Publication status	Published (VoR) - 2 Nov 2014
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 26 Aug 2014 → 30 Aug 2014

Publication series

Name	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Conference

Conference	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country/Territory	United States
City	Chicago
Period	26/08/14 → 30/08/14

Access to Document

10.1109/EMBC.2014.6943713

Cite this

Akutekwe, A., & Seker, H. (2014). A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 804-807). Article 6943713 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6943713

Akutekwe, Arinze ; Seker, Huseyin. / A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 804-807 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

@inproceedings{fd09ed61b76c4326b539a84f70c3fda0,

title = "A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis",

author = "Arinze Akutekwe and Huseyin Seker",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 ; Conference date: 26-08-2014 Through 30-08-2014",

year = "2014",

month = nov,

day = "2",

doi = "10.1109/EMBC.2014.6943713",

language = "English",

series = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "804--807",

booktitle = "2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014",

}

Akutekwe, A & Seker, H 2014, A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6943713, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 804-807, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 26/08/14. https://doi.org/10.1109/EMBC.2014.6943713

A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. / Akutekwe, Arinze; Seker, Huseyin.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 804-807 6943713 (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis

AU - Akutekwe, Arinze

AU - Seker, Huseyin

PY - 2014/11/2

Y1 - 2014/11/2

UR - https://www.scopus.com/pages/publications/84929484991

UR - https://www.scopus.com/pages/publications/84929484991#tab=citedBy

U2 - 10.1109/EMBC.2014.6943713

DO - 10.1109/EMBC.2014.6943713

M3 - Conference contribution

C2 - 25570081

AN - SCOPUS:84929484991

T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

SP - 804

EP - 807

BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014

Y2 - 26 August 2014 through 30 August 2014

ER -

Akutekwe A, Seker H. A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 804-807. 6943713. (2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014). doi: 10.1109/EMBC.2014.6943713

A hybrid dynamic Bayesian network approach for modelling temporal associations of gene expressions for hypertension diagnosis

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this