Dr. Mete, a Research Scholar in the University of Arizona College of Medicine, leverages his bioinformatics expertise to develop and implement decision-making systems for clinical settings. His recent focus lies in architecting multi-modal AI tools that integrate diverse data modalities and machine learning algorithms to increase organ transplant utilization.

Recent Publications

Predicting post-heart transplant composite renal outcome risk in adults: a machine learning decision tool, Kidney International Reports 7 (6), 1410-1415, 2022
Prediction of Outcome in Hospitalized Patients with COVID-19: Development of a COVID-19 Scoring Prognostic Model, Blood, 2022
MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review Abdominal Radiology, 2023
Clifford algebra multivectors and kernels for melanoma classification Mathematical Methods in the Applied Sciences, 2021
RNA Secondary Structure Database, Analysis Tool-Set, and Case-Study Results on SARS-CoV-2 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021
Impact of induction immunosuppression on patient survival in heart transplant recipients treated with tacrolimus and mycophenolic acid in the current allocation era, 2019
Machine learning prediction for COVID-19 disease severity at hospital admission BMC Medical Informatics and Decision Making, 2023
Predicting Clonal Evolution to Secondary Myeloid Neoplasms in Aplastic Anemia through Machine Learning, Blood, 2022
A Quaternary Classifier for the Clinical Evaluation of Pigmented Skin Lesions 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE),
Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data, Journal of neuroscience research, 2019