title: Structural prediction of dynamic Bayesian network with partial prior information.
abstract: The prediction of the structure of a hidden dynamic Bayesian network (DBN) from a noisy dataset is an important and challenging task. This work presents a generalized framework to infer the DBN network structure with partial prior information. In the proposed framework, the partial information about the network structure is provided in the form of prior. The proposed method makes use of the prior information regarding the presence and as well as absence of some of the edges. Using the noisy dataset and partial prior information, this method is able to infer nearly accurate structure of the network. The proposed method is validated using simulated datasets. In addition, two real biological datasets are used to infer hidden biological interaction networks.
authors.forename: Aniruddha
authors.initials: A
authors.lastname: Maiti
authors.forename: Ramakanth
authors.initials: R
authors.lastname: Reddy
authors.forename: Anirban
authors.initials: A
authors.lastname: Mukherjee
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D045744:Cell Line, Tumor; D030562:Databases, Protein; D004331:Drosophila melanogaster / Q000235:genetics; D015870:Gene Expression; D015233:Models, Statistical; D024510:Muscle Development / Q000235:genetics; D025941:Protein Interaction Mapping