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Mutlu Mete

Title: Associate Professor

Department: Information Science

College: College of Information

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, University of Arkansas at Little Rock, 2008
    Major: Applied Computing
    Dissertation: "Delineation of Malignant Areas in Histological Images of Head and Neck Cancer"

Current Scheduled Teaching

HINF 5637.401Introduction to Health InformaticsSpring 2025
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

HINF 5204.020Health Information Terminology and StandardsFall 2024 SPOT
Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Conference Proceeding

  • Akin, V., Mete, M. (2024). Identification of Optimal Time Domain Features for Machine Learning based Fault Classification. 2024 IEEE 17th Dallas Circuits and Systems Conference (DCAS). 1--3.
  • Akin, V., Mete, M. (2024). Investigating the Impact of Signal Resolution on Machine Learning Based Multi-Class Fault Detection. 2024 IEEE 17th Dallas Circuits and Systems Conference (DCAS). 1--4.
  • Aydin, B., Mete, M., Logue, C., Oruganti, V. (2024). Use of a High Payload Drone and Artificial Intelligence for Low Intensity Fires. ATMAE 2024.
  • Arslan, A.N., Mete, M., Kumari, A. (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). 2471--2478.
  • Mete, M., Sirakov, N., Dickson, L., Frieder, J., Griffin, J., Hosler, G.A., Menter, A. (2020). A Quaternary Classifier for the Clinical Evaluation of Pigmented Skin Lesions. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). 734--739.
  • Mete, M., Arslan, A.N. (2020). Graphical processing unit-supported rna secondary structure comparison. Proc. of the 12th Inter. Conf. on Bioinformatics and Computational Biology. 70 109--118.
  • Natarajan, K., Nguyen, T.D., Mete, M. (2018). Hand gesture controlled drones: An open source library. 2018 1st International Conference on Data Intelligence and Security (ICDIS). 168--175.
  • Mete, M., Sirakov, N.M., Griffin, J., Menter, A. (2016). A novel classification system for dysplastic nevus and malignant melanoma. 2016 IEEE International Conference on Image Processing (ICIP). 3414--3418.
  • Ramesh, S., Mete, M., Yuruk, N., Arslan, A. (2015). Density Based Visualization of Big Data With Graphical Processing Units. Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Kockara, S., Mete, M., Halic, T., Yuruk, N., Ercan, M., Lawrence, A. (2015). Fractals for Malignancy Detection in Dermoscopy Images. IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015).
  • Sirakov, N.M., Mete, M., Selvaggi, R., Luong, M. (2015). New Accurate Automated Melanoma Diagnosing Systems. IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015).
  • Ankam, H., Mete, M., Sako\uglu, \"Unal. (2014). A Compact Independent Component Analysis Implementation with GPU. 11th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Esquivel, J.E., Mete, M., Sako\uglu, \"Unal. (2014). DynaConn: A Software for Analyzing Brain’s Dynamic Functional Connectivity from fMRI Data. 11th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Sen, F., Wigand, R.T., Agarwal, N., Mete, M., Kasprzyk, R. (2014). Focal Structure Analysis in Large Biological Networks. 6th International Conference on Bioinformatics and Biomedical Technology.
  • Akg\"un, Devrim, Sako\uglu, \"Unal, Mete, M., Esquivel, J., Adinoff, B. (2014). GPU-Accelerated Dynamic Functional Connectivity Analysis for Functional MRI Data Using OpenCL. in Electro/Information Technology (EIT), 2014 IEEE International Conference on. 255--260.
  • Akgun, D., Esquivel, J.E., Mete, Mutlu, Sako\uglu, \"Unal. (2014). OpenMP-Accelerated Dynamic Functional Connectivity Analysis on Multicore Computer. 11th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Mete, M., Sirakov, N.M. (2014). Optimal set of features for accurate skin cancer diagnosis. 2014 IEEE International conference on image processing (ICIP). 2256--2260.
  • Bohra, K., Sako\uglu, \"Unal, Mete, M. (2014). Software Toolbox for Multivariate Pattern Analysis of Different Brain States from Functional Magnetic Resonance Imaging Data. Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Mete, M., Ankam, H., Sako\uglu, \"Unal. (2013). A Graphical Processing Unit Supported Neuroimaging Software in JAVA. 10th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Esquivel, J.E., Mete, M., Sako\uglu, \"Unal. (2013). Software for Analyzing Brain’s Dynamic Functional Connectivity from fMRI. Proceedings of the IEEE EMBS Annual Medical Device Symposium.
  • Chen, J., Wen, Q., Zhuo, C., Mete, M. (2012). A novel approach towards head detection of giant pandas in the free-range environment. 2012 5th International Congress on Image and Signal Processing. 814--818.
  • Wen, Q., Qu, W., Chen, J., Mete, M. (2012). A novel method for counting subcellular structures labeled by green fluorescent protein. 2012 International Conference on Computational Problem-Solving (ICCP). 500--503.
  • Mete, M., Devous, M., Spence, J., Adinoff, B. (2012). A support vector machines model to classify cocaine patients. ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH. 36 396A--396A.
  • Chen, J., Wen, Q., Pang, Z., Mete, M. (2012). An effective approach towards color image segmentation for micro-vessel detection. 2012 International Conference on Computational Problem-Solving (ICCP). 59--63.
  • Chen, J., Wen, Q., Zhuo, C., Mete, M. (2012). Automatic head detection for passenger flow analysis in bus surveillance videos. 2012 5th International Congress on Image and Signal Processing. 143--147.
  • Mete, M., Chen, J., Wen, Q., Liu, X. (2012). Color region annotation for microvessel density estimation. Wavelet Active Media Technology and Information Processing (ICWAMTIP), 2012 International Conference on. 145--148.
  • Chen, J., Wen, Q., Zhuo, C., Mete, M. (2012). Extraction of color entropy sequence for micro-vessel detection in virtual slide. 2012 5th International Congress on Image and Signal Processing. 871--875.
  • Komanduri, R.K., Mete, M. (2012). High Performance Processing of Virtual Slide on GPUs. 9th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
  • Yenialp, E., Kalkan, H., Mete, M. (2012). Improving density based clustering with multi-scale analysis. Computer Vision and Graphics: International Conference, ICCVG 2012, Warsaw, Poland, September 24-26, 2012. Proceedings. 694--701.
  • Chen, J., Wen, Q., Qu, W., Mete, M. (2012). Panda facial region detection based on topology modelling. 2012 5th International Congress on Image and Signal Processing. 911--915.
  • Chen, J., Wen, Q., Zhuo, C., Mete, M. (2012). Pose recognition of giant pandas based on gradient shapes. 2012 International Conference on Computational Problem-Solving (ICCP). 358--362.
  • Mete, M., Ou, Y., Sirakov, N.M. (2012). Skin lesion feature vector space with a metric to model geometric structures of malignancy for classification. Combinatorial Image Analaysis: 15th International Workshop, IWCIA 2012, Austin, TX, USA, November 28-30, 2012. Proceedings 15. 285--297.
  • Mete, M., Adinoff, B., Devous, M., Spence, J. (2011). A machine learning approach for patient classification in cocaine addiction via SPECT images. College on Problems of Drug Dependence.
  • Suer, S., Kockara, S., Mete, M. (2011). An improved border detection in dermoscopy images for density based clustering. BMC bioinformatics. 12 1--10.
  • Sirakov, N.M., Mete, M., Chakrader, N.S. (2011). Automatic boundary detection and symmetry calculation in dermoscopy images of skin lesions. 2011 18th IEEE International Conference on Image Processing. 1605--1608.
  • Chen, B., Nordin, B., Bobba, S., Singireddy, D., Taylor, B., Kockara, S., Mete, M. (2011). Clustering on Protein Sequence Motifs using SCAN and Positional Association Rule Algorithms. International Conference on Bioinformatics \& Computational Biology. 85--90.
  • Kockara, S., Mete, M., Chen, B., Aydin, K. (2010). Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images. BMC bioinformatics. 11 1--11.
  • Xu, X., Mete, M., Bisgin, H., Aydin, K., Agarwal, N., Schweiger, T. (2010). Finding Community Leaders in Social Networks. The fourth ACM Workshop on Social Network Mining and Analysis (SNAKDD) held in conjunction with the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). July 25, 2010, Washington DC, USA..
  • Mete, M., Sirakov, N.M. (2010). Lesion detection in demoscopy images with novel density-based and active contour approaches. BMC bioinformatics. 11 1--13.
  • Chen, B., Mete, M., Kockara, S. (2010). Parameter-Free Multi-Level Fuzzy C-Means Clustering for Unsupervised Structure Detection In Histological Images. SDPS 2010 Transformative Systems Conference.
  • Yuruk, N., Mete, M., Xu, X., Schweiger, T.A. (2009). AHSCAN: Agglomerative hierarchical structural clustering algorithm for networks. 2009 International Conference on Advances in Social Network Analysis and Mining. 72--77.
  • Mete, M., Hennings, L., Spencer, H.J., Topaloglu, U. (2009). Automatic identification of angiogenesis in double stained images of liver tissue. BMC bioinformatics. 10 1--14.
  • Kockara, S., Yip, V., Mete, M. (2009). Balls hierarchy: Image segmentation by graph spanner. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 514--517.
  • Mete, M., Topaloglu, U. (2009). Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images. 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 284--291.
  • Mete, M., Tang, F., Xu, X., Yuruk, N. (2008). A structural approach for finding functional modules from large biological networks. Bmc Bioinformatics. 9 1--14.
  • Yuruk, N., Mete, M., Xu, X., Schweiger, T.A. (2008). Finding Hierarchical Clusters in Networks. The Seventh Annual Conference on Applied Research in Information Technology. 20.
  • Yuruk, N., Mete, M., Xu, X., Schweiger, T.A. (2007). A divisive hierarchical structural clustering algorithm for networks. Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007). 441--448.
  • Mete, M., Xu, X., Fan, C., Shafirstein, G. (2007). A machine learning approach for identification of head and neck squamous cell carcinoma. 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). 29--34.
  • Mete, M., Xu, X., Fan, C., Shafirstein, G. (2007). Automatic delineation of malignancy in histopathological head and neck slides. BMC bioinformatics. 8 1--13.
  • Mete, M., Xu, X., Fan, C., Shafirstein, G. (2006). Head and neck cancer detection in histopathological slides. Sixth IEEE International Conference on Data Mining-Workshops (ICDMW'06). 223--230.
  • Xu, X., Mete, M., Yuruk, N. (2005). Mining concept associations for knowledge discovery in large textual databases. Proceedings of the 2005 ACM symposium on Applied computing. 549--550.

    • Journal Article

  • Gumus, K.Z., Menendez, M., Baerga, C.G., Harmon, I., Kumar, S., Mete, M., Hernandez, M., Ozdemir, S., Yuruk, N., Balaji, K., others. (2025). Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer. Computer Methods and Programs in Biomedicine. 259 108528. Elsevier.
  • Zhu, M., Sharma, P., Mete, M., Olcal, A., Ibrahim, I.F., Chan, W., Bat, T. (2024). Absolute Immature Platelet Count As an Accessible Diagnostic Tool for Aplastic Anemia Vs. Immune Thrombocytopenia. Blood. 144 2700. Content Repository Only!.
  • Kazim Z. Gumus, Samuel Serrano Contreras, Mohammed Al-Toubat, Ira, Harmon, Mauricio Hernandez, Savas Ozdemir, Sindhu Kumar, Nurcan, Yuruk, Mutlu Mete, K. C. Balaji, Mark Bandyk,, Gopireddy, D.R. (2024). MRI-based radiomic features for identifying recurrent prostate cancer after proton radiation therapy. Other. e14293. https://doi.org/10.1002/acm2.14293.
  • Duran, M.N., Tombul, Z., Mete, M., Toprak, A.C., Ogbue, O., Awada, H., Gurnari, C., Maciejewski, J., Ibrahim, I.F., Ozturk, M., others. (2024). Predicting Extravascular Hemolysis in Paroxysmal Nocturnal Hemoglobinuria. Blood. 144 2695. Content Repository Only!.
  • Ariyamuthu, V.K., Cheng, X.S., Hippen, B., Bloom, R.D., Acharya, D., Araj, F., Gungor, A.B., Alhamad, T., Singh, N., Anand, P.M., others. (2024). The final rule in a bind: What we are learning from suboptimal simultaneous heart-kidney outcomes and potential solutions to the problem. Other. 10--1097. LWW.
  • Abdallah, J., Williams, R.G., Awada, H., Raman, G., Ozcan, Y., Orland, M., Mete, M., Chen, W., Gurnari, C., Maciejewski, J.P., others. (2024). Thrombocytosis and megakaryocyte changes associated with PRCA. Blood Advances. 8 (12) 3058--3062. American Society of Hematology Washington, DC.
  • Ren, E., Khalilian, K.E., Mete, M., Gurnari, C., Maciejewski, J., Bravo-Perez, C., Bat, T. (2024). Using Machine Learning to Predict the Risk of Evolution to Paroxysmal Nocturnal Hemoglobinuria in Patients with Aplastic Anemia. Blood. 144 1315. Content Repository Only!.
  • R. Rajabioun, M. Afshar, \"O. Atan, M. Mete, Akin, B. (2023). Classification of Distributed Bearing Faults using a Novel Sensory Board and Deep Learning Networks with Hybrid Inputs. Other. (10.1109/TEC.2023.3338447)
  • Rajabioun, R., Afshar, M., Mete, M., Atan, \"Ozkan, Akin, B. (2023). Distributed bearing fault classification of induction motors using 2D deep-learning model. Other. IEEE.
  • Raman, G., Ashraf, B., Demir, Y.K., Kershaw, C.D., Cheruku, S., Atis, M., Atis, A., Atar, M., Chen, W., Ibrahim, I., others. (2023). Machine learning prediction for COVID-19 disease severity at hospital admission. Other. 23 (1) 46. BioMed Central London.
  • Calimano-Ramirez, L.F., Virarkar, M.K., Hernandez, M., Ozdemir, S., Kumar, S., Gopireddy, D.R., Lall, C., Balaji, K., Mete, M., Gumus, K.Z. (2023). MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Other. 48 (7) 2379--2400. Springer US New York.
  • Toprak, A.C., Bat, T., Mete, M., Gurnari, C., Bass, Z., Maciejewski, J.P., Awada, H., Olcal, A., Ibrahim, I.F. (2023). Predicting Clonal Evolution to Secondary Myeloid Neoplasms in Aplastic Anemia through Machine Learning. Blood. 142 4093. Content Repository Only!.
  • Mete, M., Ayvaci, M.U., Ariyamuthu, V.K., Amin, A., Peltz, M., Thibodeau, J.T., Grodin, J.L., Mammen, P.P., Garg, S., Araj, F., others. (2022). Predicting post-heart transplant composite renal outcome risk in adults: a machine learning decision tool. Other. 7 (6) 1410--1415. Elsevier.
  • Raman, G., Ashraf, B., Demir, Y., Kershaw, C., Cheruku, S., Atis, M., Atis, A., Atar, M., Chen, W., Ibrahim, I.F., others. (2022). Prediction of Outcome in Hospitalized Patients with COVID-19: Development of a COVID-19 Scoring Prognostic Model. Blood. 140 (Supplement 1) 11224--11225. American Society of Hematology.
  • Akar, M., Sirakov, N., Mete, M. (2021). Clifford algebra multivectors and kernels for melanoma classification. Other. https://doi.org/10.1002/mma.8034.
  • Sakoglu, U., Mete, M., Esquivel, J., Rubia, K., Briggs, R., Adinoff, B. (2019). Classification of cocaine-dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data. Other. 97 (7) 790--803.
  • Amin, A.A., Araj, F.G., Ariyamuthu, V.K., Drazner, M.H., Ayvaci, M.U., Mammen, P.P., Mete, M., Urey, M.A., Tanriover, B. (2019). Impact of induction immunosuppression on patient survival in heart transplant recipients treated with tacrolimus and mycophenolic acid in the current allocation era. Other. 33 (8) e13651.
  • Bayraktar, M., Kockara, S., Halic, T., Mete, M., Wong, H.K., Iqbal, K. (2019). Local edge-enhanced active contour for accurate skin lesion border detection. BMC Bioinformatics. 20 87--97. BioMed Central.
  • Ariyamuthu, V.K., Amin, A.A., Drazner, M.H., Araj, F., Mammen, P.P., Ayvaci, M., Mete, M., Ozay, F., Ghanta, M., Mohan, S., others. (2018). Induction regimen and survival in simultaneous heart-kidney transplant recipients. Other. 37 (5) 587--595. Elsevier.
  • Kaya, S., Bayraktar, M., Kockara, S., Mete, M., Halic, T., Field, H.E., Wong, H.K. (2016). Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images. BMC Bioinformatics. 17 95--115. BioMed Central.
  • Tanriover, B., MacConmara, M.P., Parekh, J., Arce, C., Zhang, S., Gao, A., Mufti, A., Levea, S., Sandikci, B., Ayvaci, M.U., others. (2016). Simultaneous liver-kidney transplantation in liver transplant candidates with renal dysfunction: importance of creatinine levels, dialysis, and organ quality in survival. Other. 1 (4) 221--229. Elsevier.
  • Mete, M., Sakoglu, U., Spence, J.S., Devous, M.D., Harris, T.S., Adinoff, B. (2016). Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach. BMC Bioinformatics. 17 49--61. BioMed Central.
  • Lemon, J., Kockara, S., Halic, T., Mete, M. (2015). Density-based parallel skin lesion border detection with webCL. Other. 16
  • Akg\"un, Devrim, Sako\uglu, \"Unal, Esquivel, J., Adinoff, B., Mete, M. (2015). GPU accelerated dynamic functional connectivity analysis for functional MRI data. Computerized Medical Imaging and Graphics. 43 53--63. Pergamon.
  • Tanriover, B., Zhang, S., MacConmara, M., Gao, A., Sandikci, B., Ayvaci, M.U., Mete, M., Tsapepas, D., Rajora, N., Mohan, P., others. (2015). Induction therapies in live donor kidney transplantation on tacrolimus and mycophenolate with or without steroid maintenance. Other. 10 (6) 1041--1049. LWW.
  • Sirakov, N.M., Ou, Y., Mete, M. (2015). Skin lesion feature vectors classification in models of a Riemannian manifold. Other. 75 217--229. Springer International Publishing.
  • Mete, M., Xu, X., Fan, C., Shafirstein, G. (2014). A machine learning approach for identification of head and neck squamous cell carcinoma (November, pg 29, 2007). Other. 36 (6) 323--323. SCI PRINTERS \& PUBL INC PO DRAWER 12425 8342 OLIVE BLVD, ST LOUIS, MO 63132 USA.
  • Kockara, S., Mete, M., Suer, S. (2013). Color and Spatial Features Integrated Normalized Distance for Density Based Border Detection in Dermoscopy Images. Other. 41--61. Springer Netherlands.
  • Mete, M., Sirakov, N.M. (2012). Dermoscopic diagnosis of melanoma in a 4D space constructed by active contour extracted features. Computerized Medical Imaging and Graphics. 36 (7) 572--579. Pergamon.
  • Mete, M., Kockara, S., Aydin, K. (2011). Fast density-based lesion detection in dermoscopy images. Computerized Medical Imaging and Graphics. 35 (2) 128--136. Pergamon.
  • Nuthakki, M.K., Mete, M., Varol, C., Suh, S.C. (2011). Uxsom: Uml generated xml to software metrics. Other. 36 (3) 1--6. ACM New York, NY, USA.
  • Kockara, S., Mete, M., Yip, V., Lee, B., Aydin, K. (2010). A soft kinetic data structure for lesion border detection. Other. 26 (12) i21--i28. Oxford University Press.
  • Yip, V., Mete, M., Topaloglu, U., Kockara, S. (2010). Concept discovery for pathology reports using an N-gram model. Other. 2010 43. American Medical Informatics Association.
  • Mete, M., Tang, F., Xu, X., Yuruk, N. (2010). Finding Functional Modules. Other. 253--273. Springer New York.
  • Murphy, R.F., Bateman, A., Lengauer, T., Moreau, Y., Durand, D., Margalit, H., Xing, E., Eskin, E., Halperin, E., Sharan, R., others. (2010). ISMB 2010 ORGANIZATION.
  • Mete, M., Yuruk, N., Xu, X., Berleant, D. (2010). Knowledge discovery in textual databases: A concept-association mining approach. Other. 225--243. Springer US.

    • Other

  • Dickson, L., Frieder, J., Griffin, J., Hosler, G., Mete, M., Sirakov, N., Menter, A. (2017). A novel automated dermoscopy-based image analyzer for the clinical evaluation of pigmented lesions and early detection of melanoma. Other.
  • Shafirstein, G., Xu, X., Mete, M. (2010). Image processing apparatus and method for histological analysis.

    • Ph.D. Thesis

  • Mete, M. (2008). Delineation of malignant areas in histological images of head-neck cancer. University of Arkansas at Little Rock.
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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