BACKGROUND Comorbidity is the phenomenon of two or more diseases occurring simultaneously not by random chance and presents great challenges to accurate diagnosis and treatment. As an effort toward better understanding the genetic causes of comorbidity, in this work, we have developed a computational method to predict comorbid diseases. Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity. METHODS To fully incorporate structural characteristics of interactome as features into prediction of comorbidity, our method embeds the human interactome into a high dimensional geometric space with weights assigned to the network edges and uses the projection onto different dimension to "fingerprint" disease modules. A supervised machine learning classifier is then trained to discriminate comorbid diseases versus non-comorbid diseases. RESULTS In cross-validation using a benchmark dataset of more than 10,000 disease pairs, we report that our model achieves remarkable performance of ROC score = 0.90 for comorbidity threshold at relative risk RR = 0 and 0.76 for comorbidity threshold at RR = 1, and significantly outperforms the previous method and the interactome generated by annotated data. To further incorporate prior knowledge pathways association with diseases, we weight the protein-protein interaction network edges according to their frequency of occurring in those pathways in such a way that edges with higher frequency will more likely be selected in the minimum spanning tree for geometric embedding. Such weighted embedding is shown to lead to further improvement of comorbid disease prediction. CONCLUSION The work demonstrates that embedding the two-dimension planar graph of human interactome into a high dimensional geometric space allows for characterizing and capturing disease modules (subgraphs formed by the disease associated genes) from multiple perspectives, and hence provides enriched features for a supervised classifier to discriminate comorbid disease pairs from non-comorbid disease pairs more accurately than based on simply the module separation.

中文翻译：

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使用人类相互作用组的加权几何嵌入来预测共病。

背景技术合并症是两种或两种以上疾病非随机同时发生的现象，对准确诊断和治疗提出了巨大挑战。为了更好地了解合并症的遗传原因，在这项工作中，我们开发了一种预测合并症的计算方法。两种具有共同基因的疾病往往会增加其合并症。先前的工作表明，将相关基因映射到人类相互作用组上后，两个疾病模块（子图）之间的距离与合并症相关。方法为了将相互作用组的结构特征作为特征完全纳入合并症的预测中，我们的方法将人类相互作用组嵌入到高维几何空间中，并将权重分配给网络边缘，并使用到不同维度的投影来“指纹”疾病模块。然后训练有监督的机器学习分类器来区分共病疾病和非共病疾病。结果在使用超过 10,000 个疾病对的基准数据集进行交叉验证时，我们报告说，我们的模型在相对风险 RR = 0 时的合并症阈值中实现了 ROC 评分 = 0.90 的卓越性能，在 RR = 1 时的合并症阈值中实现了 0.76 的显着性能，并且显着优于之前的方法和由注释数据生成的交互组。为了进一步纳入与疾病相关的先验知识路径，我们根据蛋白质-蛋白质相互作用网络边在这些路径中出现的频率对它们进行加权，这样频率较高的边更有可能在最小生成树中被选择用于几何嵌入。这种加权嵌入被证明可以进一步改善共病疾病的预测。结论这项工作表明，将人类相互作用组的二维平面图嵌入到高维几何空间中可以从多个角度表征和捕获疾病模块（由疾病相关基因形成的子图），从而为监督分类器提供丰富的特征比简单地基于模块分离更准确地区分共病疾病对和非共病疾病对。