Huang Zhipeng, Xu Kevin S
Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106 USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022 Dec;2022:1020-1027. doi: 10.1109/bibm55620.2022.9995514. Epub 2023 Jan 2.
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 grant 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 , 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.
肾移植是终末期肾病患者的首选治疗方法。随着时间的推移，成功的肾移植仍会失败，即移植失败；然而，移植失败的时间，即移植物存活时间，在不同受者之间可能有很大差异。影响移植物存活时间的一个重要生物学因素是供体和受体的人类白细胞抗原（HLA）之间的相容性。我们建议使用网络对HLA相容性进行建模，其中节点表示供体和受体的不同HLA，边权重表示HLA的相容性，其可以是正的或负的。该网络是，因为边权重是从移植结果估计而不是直接观察到的。我们为这种间接观察到的加权和有符号网络提出了一个潜在空间模型。我们证明，我们的潜在空间模型不仅可以更准确地估计HLA相容性，还可以纳入生存分析模型，以提高预测移植物存活时间下游任务的准确性。
