Department of Electrical & Computer Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA.
Biomedical Engineering, University of Puerto Rico, Mayaguez, PR 00681-9000, USA.
Genes (Basel). 2022 Mar 8;13(3):473. doi: 10.3390/genes13030473.
Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin.
骨骼肌萎缩是衰老、糖尿病和长期太空飞行中微重力导致的常见病症。本文通过链接预测算法研究多模态基因疾病和疾病药物网络，以选择药物进行重新利用来治疗骨骼肌萎缩。使用图论网络分析，从 GeneLab 提供的从太空飞行中采集的转录组数据集，检测导致左右伸趾长肌、比目鱼肌、股四头肌和左右跖肌组织中肌肉萎缩的关键靶基因。通过皮尔逊相关和贝叶斯马尔可夫毯方法确定顶级肌肉萎缩基因调节剂。使用可扩展精准医学知识引擎构建基因疾病知识图谱。我们计算了节点嵌入、网络中的随机游走度量。使用嵌入、网络特征来训练图卷积网络、图神经网络、随机森林和梯度提升方法，以预测链接和对骨骼肌萎缩的顶级基因-疾病关联进行排名。选择药物并构建疾病药物知识图谱。链接预测方法应用于疾病药物网络，以识别治疗骨骼肌萎缩的顶级候选药物。基于接收者操作特征曲线和预测精度，图卷积网络在链接预测中表现最佳。涉及骨骼肌萎缩的关键基因与代谢和神经退行性疾病有关。使用图卷积网络方法选择的用于重新利用的药物是营养物、皮质类固醇、抗炎药物和其他与胰岛素相关的药物。
