A machine learning approach to predict pancreatic islet grafts rejection versus tolerance - Ceballos, Gerardo A.; Hernandez, Luis F.; Paredes, Daniel; Betancourt, Luis R.; Abdulreda, Midhat H. (PloS one, 2020)
Keywords: Aging; Allografts; Analytical chemistry; Animals; Anterior chamber; Aqueous humour; Artificial Intelligence; Biology and Life Sciences; Biomarkers; Biomedical research; Categories; Chromatography; Computer and Information Sciences; Computer programs; Diabetes; Diabetes Mellitus, Experimental - immunology; Digital video recorders; Disease; Electrokinetics; Engineering and Technology; FDA approval; Feasibility; Female; Fluorescence; Forecasting - methods; Forecasts and trends; Graft rejection; Graft Rejection - physiopathology; Graft Survival - immunology; Health aspects; Immune Tolerance; Immunological tolerance; Immunosuppression Therapy - methods; Islet cell transplantation; Islets of Langerhans - immunology; Islets of Langerhans - metabolism; Islets of Langerhans Transplantation - methods; Isoantigens - immunology; Laser induced fluorescence; Learning algorithms; Machine Learning; Male; Medical research; Medicine; Medicine and Health Sciences; Mice; Mice, Inbred C57BL; Mice, Inbred DBA; Pancreas transplantation; Pancreatic islet transplantation; Patient outcomes; Pattern recognition; Rejection; Software; Support Vector Machine; Support vector machines; Transplantation, Homologous; Transplants & implants; United States; Venezuela; United States--US