Bayesian network prior: network analysis of biological data using external knowledge.
BACKGROUND
Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event 'gene interaction' and is used to calculate the probability of a candidate graph (G) in the structure learning process.


RESULTS
Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods.


BACKGROUND
Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP.


BACKGROUND
hasan.otu@bilgi.edu.tr


BACKGROUND
Supplementary data are available at Bioinformatics online.
Bogazici University, Institute of Biomedical Engineering, Kandilli Campus, 34684, Cengelkoy - Istanbul, TUBITAK-BILGEM, Informatics and Information Security Research Center, 41470, Gebze-Kocaeli and Istanbul Bilgi University, Department of Genetics and Bioengineering, 34060, Eyup - Istanbul, Turkey.
Senol
S
Isci
Haluk
H
Dogan
Cengizhan
C
Ozturk
Hasan H
HH
Otu
D001499:Bayes Theorem; D002292:Carcinoma, Renal Cell / Q000235:genetics; D015870:Gene Expression; D053263:Gene Regulatory Networks; D023281:Genomics; D006801:Humans; D007680:Kidney Neoplasms / Q000235:genetics; D011336:Probability; D012984:Software