Development of Genetic Network Reconstruction Algorithm for Small Sample Size Based on Bayesian Networks

• Main Authors: Chung-Chi Huang, 黃政基
• Other Authors: 陳中明
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/84926669256581677006

碩士 === 國立臺灣大學 === 醫學工程學研究所 === 94 === With the continual progress of human genome researches, more and more genes have been found to be closely related to human diseases. Accordingly, exploration of genetic functions has become one of the major foci in biotechnology researches. It is well known that each gene does not work alone. Instead, it may involve enormous complicated interactions among genes in a biological process. Because of the complexity of physiological and biochemical processes in the human body, the relations between the genes and most diseases are not clear currently. Therefore, the ultimate goal of gene network reconstruction is to analyze the regulatory mechanisms among genes and understand how genes involve in biological processes. Limited by the high cost of microarrays, most biological experiments can not offer a large number of observations for gene network reconstruction. To overcome this limitation, a new gene network reconstruction algorithm, called Divide-and-conquer Variational Bayesian (DCVB) algorithm, is proposed in this study. Although the VB algorithm, which is the basic construct of DCVB, has been shown to be effective for long time-course data, its performance for short time-course data is far from satisfactory. The DCVB algorithm decomposes the large gene networks into multiple small subnets. By considering those genes not included in a subnet as latent factors, the DCVB algorithm is capable of estimating gene-gene interactions for each subnet independently, thanks to the ability of the VB algorithm in incorporating latent factors. Two classes of DCVB algorithms will be evaluated, namely, single-level and hierarchical DCVB. While the former decomposes the entire network into small subnets of fixed sizes for reconstruction, the latter integrates the results of multiple levels, each with a different network size, to form the final reconstructed network. Because DCVB does not estimate all gene-gene interactions for the entire network at a time, the number of parameters to be estimated is greatly reduced compared to the conventional VB algorithm. It thus promises a better performance for reconstructing a large network with short time-course data than the VB algorithm. Performance comparison between the DCVB and VB is carried out by using simulated time-course data and p53R2 experimental data. For the simulated data, three gene networks with various lengths of time-course data are simulated. According to the simulation results, the proposed DCVB outperforms the VB for both short and long time-course data. Especially, the DCVB is substantially superior to the VB for large networks and long time-course data. For the data of p53R2 study, it requires further experiments to validate the networks reconstructed by the DCVB and the VB, respectively. In summary, the DCVB is shown to be better than the VB only for the simulation data. Further validations are required for the performance comparison between both algorithms for the real data.