CAJ | 학술논문

为了有效提高基因调控网络推断的精度，基于基因表达数据和基因扰动数据，将基因调控网络建模为结构方程模型，并进一步转化为验证性因子分析（CFA）模型，然后使用贝叶斯方法求解 CFA 模型参数。在贝叶斯分析中，为减少计算量，不采用常用的马尔科夫蒙特卡洛抽样方法，而是采用变分逼近技术对参数的联合后验分布进行因式化，并获得参数的后验包含概率分布及参数的后验分布。同时使用重要性抽样技术对 CFA 模型的推断参数进行加权平均。仿真结果表明，CFA 模型和变分逼近技术是有效和可靠的。根据实验数据，使用所提算法推导了具有35个基因的酵母基因调控网络。
위료유효제고기인조공망락추단적정도，기우기인표체수거화기인우동수거，장기인조공망락건모위결구방정모형，병진일보전화위험증성인자분석（CFA）모형，연후사용패협사방법구해 CFA 모형삼수。재패협사분석중，위감소계산량，불채용상용적마이과부몽특잡락추양방법，이시채용변분핍근기술대삼수적연합후험분포진행인식화，병획득삼수적후험포함개솔분포급삼수적후험분포。동시사용중요성추양기술대 CFA 모형적추단삼수진행가권평균。방진결과표명，CFA 모형화변분핍근기술시유효화가고적。근거실험수거，사용소제산법추도료구유35개기인적효모기인조공망락。
To improve the inference accuracy of gene regulatory networks (GRN),using both gene perturbations and gene expression data,GRN is modeled as a structural equation model (SEM),and further transformed into the confirmatory factor analysis (CFA)model.The Bayesian approach is used to infer the parameters of the regulatory networks.Instead of the Markov chain Monte Carlo (MCMC)method,the variational approximation method (VAM)is applied for its lower computa-tion cost,which factorizes the joint posterior distribution of parameters,and obtains the posterior in-clusion probability distribution and the posterior distribution of parameters.An importance sampling technique is then applied to obtain the weighted average of the CFA inferred parameters.Simulations are carried out to verify the effectiveness and reliability of the CFA model and the variational approx-imation.Based on the experimental data,the regulatory interactions among a 35 yeast genes network are identified with the proposed VAM algorithm.