We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed. The Molecular Pathways triggered by these genes are identified. The Bayesian inference posteriors distributions are estimated using a variational analytical algorithm and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and possible future work are concluded.

翻译：我们利用概率图模型和贝叶斯推断，结合分子基因表达研究与公共数据库进行疾病建模。以脊髓性肌萎缩症全基因组关联分析结果为案例，构建并分析了疾病发展的两个阶段中上下调基因与公共领域已有知识的关联性及共表达网络，鉴定出这些基因所触发的分子通路。通过变分分析算法与马尔可夫链蒙特卡洛采样算法估算了贝叶斯推断的后验分布，并总结了当前假设、局限性及未来研究方向。