The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based h

Background: To further understand the implementation of hyperparameters re-estimation technique in Bayesian hierarchical model, we added two more prior assumptions over the weight in BayesPI, namely Laplace prior and Cauchy prior, by using the evidence approximation method. In addition, we divided hyperparameter (regularization constants a of the model) into multiple distinct classes based on either the structure of the neural networks or the property of the weights. Results: The newly implemented BayesPI was tested on both synthetic and real ChIP-based high-throughput datasets to identify the corresponding protein binding energy matrices. The results obtained were encouraging: 1) there was a minor effect on the quality of predictions when prior assumptions over the weights were altered (e.g. the prior probability distributions to the weights and the number of classes to the hyperparameters) in BayesPI; 2) however, there was a significant impact on the computational speed when tuning ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Junbai Wang
• Keywords: Bayesian Hierarchical Model, BMCBI 2010, Evidence Approximation Method, Prior Assumptions