Machine Learning and Network Science for Predicting Kidney Transplant Survival

慢性肾脏疾病影响美国约10%的成年人和7-12%的人口 国际吧它可能导致不可逆的肾功能丧失，称为终末期肾病（ESRD）。 对于患有ESRD的患者，在以下方面，与透析相比，肾移植是首选治疗： 患者生存率、生活质量和成本。尽管肾移植有很多优点，但大多数患者 ESRD主要通过透析治疗，因为相容供体数量不足 对患者已知器官供体和受体的人类白细胞抗原（HLA）是一种免疫调节剂。 由于免疫原性，移植器官存活时间的重要影响因素是免疫原性， 受体对移植器官的反应。供体和受体HLA之间的错配是 与较短的生存时间相关;然而，识别具有完美的 所以大多数移植涉及HLA不匹配。 我们的主要目标是准确预测肾移植的存活时间通过整合两个数据- 基于过去移植结果的HLA相容性驱动模型和生物驱动模型 HLA免疫原性。准确预测生存时间可以通过以下方式改善患者移植结果： 更有效地分配捐助者和受援者，特别是通过减少重复捐助的次数， 由于移植失败而导致的器官移植。我们建议评估HLA相容性 使用应用于过去移植结果的高维变量选择技术， HLA兼容性网络的新的供体-受体潜在空间模型。然后，我们建议将 这些预测的相容性沿着生物驱动的HLA免疫原性模型， 酸序列和表位到一个多任务分类为基础的生存预测算法。我们 用于学习移植存活的集成数据和生物驱动模型的建议方法 广泛地推广到器官移植（肝脏、心脏、胰腺、肺），可能还包括骨髓移植 移植

Chronic kidney disease affects about 10% of adults in the United States and 7-12% of the population worldwide. It may lead to irreversible loss of kidney function, known as end-stage renal disease (ESRD). For patients with ESRD, kidney transplantation is the preferred treatment compared to dialysis in terms of patient survival, quality of life and cost. Despite the advantages of kidney transplants, most patients with ESRD are treated with dialysis primarily because there exist an insufficient number of compatible donors for patients. The human leukocyte antigens (HLAs) of the organ donor and recipient are known to be a significant contributing factor to transplanted organ survival times due to immunogenicity, the immune response of the recipient to the transplanted organ. Mismatches between donor and recipient HLAs are associated with shorter survival times; however, it is extremely rare to identify donors that have a perfect match with recipients, so most transplants involve mismatched HLAs. Our main objective is to accurately predict survival times for kidney transplants by incorporating both data- driven models of HLA compatibility based on outcomes of past transplants and biologically-driven models of HLA immunogenicity. Accurate prediction of survival times can improve patient transplant outcomes by enabling more efficient allocation of donors and recipients, particularly by reducing the number of repeat transplants due to graft failure with a poorly matched donor. We propose to estimate HLA compatibilities using high-dimensional variable selection techniques applied to outcomes of past transplants and through a novel donor-recipient latent space model for the HLA compatibility network. We then propose to incorporate these predicted compatibilities along with biologically-driven models of HLA immunogenicity using amino acid sequences and epitopes into a multi-task classification-based survival prediction algorithm. Our proposed approach for learning integrated data- and biologically-driven models of transplant survival generalizes broadly to organ transplantation (liver, heart, pancreas, lungs) and possibly to bone marrow transplantation.