In recent years there has been great interest in making inference for gene expression data collected over time. In this article, we describe a Bayesian hierarchical mixture model for partitioning such data. While conventional approaches cluster the observed data, we assume a nonparametric, random walk model, and partition on the basis of the parameters of this model. The model is flexible and can be tuned to the specific context, respects the order of observations within each curve, acknowledges measurement error, and allows prior knowledge on parameters to be incorporated. The number of partitions may also be treated as unknown, and inferred from the data, in which case computation is carried out via a birth-death Markov chain Monte Carlo algorithm. We first examine the behavior of the model on simulated data, along with a comparison with more conventional approaches, and then analyze meiotic expression data collected over time on fission yeast genes.

ABSTRACT. The Bayesian network (BN) is a knowledge representation form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal relationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most successful. Nonetheless, the performance of the K2 algorithm is greatly affected by a prior ordering of input nodes. The proposed method in this paper is based on the chaotic particle swarm optimization (CPSO) and the K2 algorithm. Because the PSO algorithm completely entraps the local minimum in later evolutions, we combined the PSO algorithm with the chaos theory, which has the properties of ergodicity, randomness, and regularity. Experimental results show that the proposed method can improve the convergence rate of particles and identify networks more efficiently and accurately.

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In the current era, big data play a very important role in various government organizations. Therefore, it is necessary to connect government big data with the national modern governance system since it is a crucial resource, asset, technology, and service support facility for government departments. The government information system requires complex system engineering. The collaborative governance of government data by integrating cognitive computing and complex networks is conducive to eliminating the contradiction and tension between policies and policy subjects. It can reduce the cross and overlap of functions between different departments and form a collaborative mechanism of other governance subjects. The suggested mechanism enhanced the government governance content integrity and system synergy. The modernization of the system is also boosted by governance capacity.