ArticleStatistics in biosciences2017
A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival.
Article in Statistics in biosciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- The transformative potential of artificial intelligence in solid organ transplantation.Frontiers in transplantation · 2024Review
- Article
- Artificial intelligence and kidney transplantation.World journal of transplantation · 2021Review
- Establishment of a prognostic-related microRNAs risk model for glioma by bioinformatics analysis.Annals of translational medicine · 2021Article
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- Identification and validation of a prognostic four-genes signature for hepatocellular carcinoma: integrated ceRNA network analysis.Hepatology international · 2019Article
- Statistical Methods in Organ Failure and Transplantation.Statistics in biosciences · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
Identifying novel biomarkers to predict renal graft survival is important in post-transplant clinical practice. Serum creatinine, currently the most popular surrogate biomarker, offers limited information of the underlying allograft profiles. It is known to perform unsatisfactorily to predict renal function. In this paper, we apply a LASSO machine-learning algorithm in the Cox proportional hazards model to identify promising proteins that are associated with the hazard of allograft loss after renal transplantation, motivated by a clinical pilot study that collected 47 patients receiving renal transplants at the University of Michigan Hospital. We assess the association of 17 proteins previously identified by Cibrik et al. [5] with allograft rejection in our regularized Cox regression analysis, where the LASSO variable selection method is applied to select important proteins that predict the hazard of allograft loss. We also develop a post-selection inference to further investigate the statistical significance of the proteins on the hazard of allograft loss, and conclude that two proteins KIM-1 and VEGF-R2 are important protein markers for risk prediction.
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