Gene Networks Reconstruction based on Structural Equation Modeling

• Main Authors: Chih-Jung Chang, 張志榮
• Other Authors: Chung-Ming Chen
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/21699798502766537048

碩士 === 臺灣大學 === 醫學工程學研究所 === 96 === 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 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 biology, the relations between the genes and most diseases are not clear currently. Therefore, the ultimate goal of gene networks 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 model：linear dynamic factor model, which is based on structural equation modeling, is proposed in this study. Besides observed variables, linear dynamic factor model also incorporates hidden factors to depict regulations from proteins and other molecules that are not included in the gene networks but have influence on the gene networks. We simulated data from a 6-gene network with different observations to see the influence of the number of observations on the performance of the algorithm. We also applied the algorithm to microarray data to reconstruct the gene networks from focal adhesion pathway、SGS1 and its synthetic sick or lethal(SSL) partners and G2/M DNA damage checkpoint of Saccharomyces cerevisiae. For the simulated data with 14 observations, the performance of the algorithm is well；for the simulated data with 52 observations, the performance of the algorithm is better than that of the simulated data with 14 observations. For the microarray data, the sensitivity or true positive rate can be in the neighborhood of 50%.