Mutlu Mete, Ph.D.

Associate Professor

Mutlu Mete, Ph.D., is a bioinformatician with a background in data mining and machine learning. He has extensive experience with machine learning applications in big data problems in modalities including tumor images, graphs interaction, strings, texts, protein, functional magnetic resonance imaging (fMRI), and SPECT. He has attended UALR and proudly worked with Dr. Xiaowei Xu. Mete has successfully completed numerous real-world biomedical research projects. During his 10+ years of faculty appointment, he has taught neural networks, general programming, image processing, mobile programming, web programming, database programming, data structures, human-computer interaction and microcomputer applications.

Educational Background

• Ph.D., Applied Science, University of Arkansas at Little Rock, 2008
• B.S., Computer Science, Dokuz Eylul University, 2001

Academic Positions

• Associate Professor, Texas A&M University-Commerce

Awards and Honors

• Teaching Excellence, Texas A&M University-Commerce, 2011

Research Interests

• Data mining and knowledge discovery
• Computer vision
• Bioinformatics
• Complex networks
• Grid computing

Research Funding

External

• $30,000, National Natural Science Foundation of China, 2012-2013
• $132,934, National Institute of Health, 2011-2013
• $24,000, The Scientific & Technological Research Council of Turkey, 2012-2013

Internal

• $12,963, Texas A&M University-Commerce, 2011-2012
• $14,533, Texas A&M University-Commerce, 2010-2011
• $13,733, Texas A&M University-Commerce, 2012-2013
• $4,000, Texas A&M University-Commerce, 2011
• $4,000, Texas A&M University-Commerce, 2011
• $4,000, Texas A&M University-Commerce, 2011

Selected Publications

• V. K. Ariyamuthu, A. A. Amin, M. H. Drazner, F. Araj, P. P. Mammen, M. Ayvaci, M. Mete, et al., Induction regimen and survival in simultaneous heart-kidney transplant recipients, The Journal of Heart and Lung Transplantation, vol. 37, pp. 587-595, 2018.
• B. Tanriover, M. P. MacConmara, J. Parekh, C. Arce, S. Zhang, A. Gao, M. Mete, et al., Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival, Kidney international reports, vol. 1, pp. 221-229, 2016.
• M. Mete, Ü. Sakoğlu, J. S. Spence, M. D. Devous, T. S. Harris, and B. Adinoff, Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach, in BMC bioinformatics, 2016, p. 357.
• S. Kaya, M. Bayraktar, S. Kockara, M. Mete, T. Halic, H. E. Field, et al., Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images, in BMC Bioinformatics, 2016, p. 367.
• B. Tanriover, S. Zhang, M. MacConmara, A. Gao, B. Sandikci, M. U. Ayvaci, M. Mete, et al., Induction therapies in live donor kidney transplantation on tacrolimus and mycophenolate with or without steroid maintenance, Clinical Journal of the American Society of Nephrology, p. CJN. 08710814, 2015.
• N. M. Sirakov, Y.-L. Ou, and M. Mete, Skin lesion feature vectors classification in models of a Riemannian manifold, Annals of Mathematics and Artificial Intelligence, vol. 75, pp. 217-229, 2015.
• J. Lemon, S. Kockara, T. Halic, and M. Mete, Density-based parallel skin lesion border detection with webCL, BMC Bioinformatics 2015, 16(Suppl 13):S5 doi:10.1186/1471-2105-16-S13-S5, vol. 16, 2015.
• D. Akgün, Ü. Sakoğlu, J. Esquivel, B. Adinoff, and M. Mete, GPU accelerated dynamic functional connectivity analysis for functional MRI data, Computerized Medical Imaging and Graphics, vol. 43, pp. 53-63, 2015.