Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of possibilities. Thus, computational methods have seen increasing applications in predicting gene-disease associations. We proposed a bioinformatics framework, Prioritization of Autism-genes using Network-based Deep-learning Approach (PANDA). Our approach aims to identify autism-genes across the human genome based on patterns of gene-gene interactions and topological similarity of genes in the interaction network. PANDA trains a graph deep learning classifier using the input of the human molecular interaction network and predicts and ranks the probability of autism association of every node (gene) in the network. PANDA was able to achieve a high classification accuracy of 89%, outperforming three other commonly used machine learning algorithms. Moreover, the gene prioritization ranking list produced by PANDA was evaluated and validated using an independent large-scale exome-sequencing study. The top 10% of PANDA-ranked genes were found significantly enriched for autism association.

中文翻译：

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熊猫：使用基于网络的深度学习方法对自闭症基因进行优先排序。

了解复杂疾病和疾病的遗传背景在有前途的精密医学中起着至关重要的作用。但是，鉴于多种可能性，评估候选基因需要耗时且昂贵的实验。因此，计算方法已在预测基因-疾病关联中得到越来越多的应用。我们提出了一个生物信息学框架，即使用基于网络的深度学习方法（PANDA）对自闭症基因进行优先排序。我们的方法旨在基于基因-基因相互作用的模式和相互作用网络中基因的拓扑相似性，在整个人类基因组中识别自闭症基因。PANDA使用人类分子相互作用网络的输入来训练图深度学习分类器，并预测和排名网络中每个节点（基因）的自闭症关联概率。PANDA能够达到89％的高分类精度，优于其他三种常用的机器学习算法。此外，使用独立的大规模外显子组测序研究评估和验证了PANDA产生的基因优先排序列表。PANDA排名前10％的基因被发现明显丰富了自闭症。使用独立的大规模外显子组测序研究评估和验证了PANDA产生的基因优先排序列表。PANDA排名前10％的基因被发现明显丰富了自闭症。使用独立的大规模外显子组测序研究评估和验证了PANDA产生的基因优先排序列表。PANDA排名前10％的基因被发现明显丰富了自闭症。