Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to graft failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.

翻译：肾移植是患有最终肾脏疾病的人的首选治疗。成功的肾移植在一段时间内仍然失败,称为移植失败;然而,移植失败的时间或移植存活时间在不同接受者之间可能有很大差异。影响移植存活时间的重大生物因素是捐赠者和接受者人类液化抗原(HLAs)之间的兼容性。我们提议使用一个网络来模拟HLA兼容性,其中捐赠者和接受者的不同HLA的结点表示不同的HLA,而边缘重量表示HLA的相容性,可以是正的,也可以是负的。这个网络被间接观察到,因为边缘重量是从移植结果而不是直接观察到的。我们为这种间接观测到的加权和签名网络提出了一个潜在的空间模型。我们证明,我们潜在的空间模型不仅能够产生对HLA相容性的更准确的估计,而且还可以纳入生存分析模型,以提高预测移植生存时间的下游任务的准确性。