A Bayesian framework for data and hypotheses driven fusion of high throughput data: application to mouse organogenesis.
In this paper we present a framework for integrating diverse data sets under a coherent probabilistic setup. The necessity of a probabilistic modeling arises from the fact that data integration does not restrict to compiling information from data bases with data that are typically thought to be non-random. Currently wide range of experimental data is also available however rarely these data sets can be summarized in simple output data, e.g. in categorical form. Moreover it may not even be appropriate to do so. The proposed setup allows modeling not only the observed data and parameters of interest but most importantly to incorporate prior knowledge. Additionally the setup easily extends to facilitate more popular data-driven analysis.
Bhattacharjee
Madhuchhanda
M
School of Mathematics and Statistics, University of St. Andrews, St. Andrews, Fife, Scotland, KY16 9SS, UK.
Pritchard
Colin
C
Nelson
Peter
P
D000728:Androgens / Q000378:metabolism; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D008297:Male; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D038081:Organogenesis; D011467:Prostate / Q000196:embryology / Q000254:growth & development / Q000378:metabolism
D000728:Androgens