A machine learning approach to predict pancreatic islet grafts rejection versus tolerance

• Published in: PloS one Vol. 15; no. 11; p. e0241925
• Main Authors: Ceballos, Gerardo A., Hernandez, Luis F., Paredes, Daniel, Betancourt, Luis R., Abdulreda, Midhat H.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.11.2020; Public Library of Science (PLoS)
• Subjects: 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0241925

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.