Influence of prior knowledge in constraint-based learning of gene regulatory networks.
Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.
Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey. mtan@etu.edu.tr
Mehmet
M
Tan
Mohammed
M
Alshalalfa
Reda
R
Alhajj
Faruk
F
Polat
D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D015398:Signal Transduction; D014157:Transcription Factors; D048490:raf Kinases / Q000378:metabolism
D014157:Transcription Factors; D048490:raf Kinases