Mutlu Mete

Previous Scheduled Teaching

Published Intellectual Contributions

Book Chapter

• Kockara, S., Mete, M., Suer, S. (2013). Color and Spatial Features Integrated Normalized Distance for Density Based Border Detection in Dermoscopy Images. Lecture Notes in Computational Vision and Biomechanics. 41-61. Springer Netherlands. https://doi.org/10.1007/978-94-007-5389-1_3
• Yenialp, E., Kalkan, H., Mete, M. (2012). Improving Density Based Clustering with Multi-scale Analysis. Lecture Notes in Computer Science. 694-701. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_83
• Mete, M., Ou, Y., Sirakov, N.M. (2012). Skin Lesion Feature Vector Space with a Metric to Model Geometric Structures of Malignancy for Classification. Lecture Notes in Computer Science. 285-297. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34732-0_22
• Mete, M., Tang, F., Xu, X., Yuruk, N. (2010). Finding Functional Modules. Systems Biology. 253-273. Springer New York. https://doi.org/10.1007/978-1-4419-5797-9_10
• Mete, M., Yuruk, N., Xu, X., Berleant, D. (2009). Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach. International Series in Operations Research & Management Science. 225-243. Springer US. https://doi.org/10.1007/978-1-4419-0176-7_11

Conference Proceeding

• Akbari, H., Pachori, R.B., Mete, M. (2025). Decoding EEG Signals to Predict SSRI Therapy Success in Depression Using Automated Tunable Q-Factor Wavelet Transform and Centered Correntropy. 2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE). 635-639. IEEE. https://doi.org/10.1109/bibe66822.2025.00110
• Mete, M., Akbari, H., Yuruk, N. (2025). Optimal Set of Time-Domain Features of EEG Signal Predicts Outcome of Depression Therapy. 2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE). 627-634. IEEE. https://doi.org/10.1109/bibe66822.2025.00109
• Mete, M., Harmon, I., Al-Toubat, M., Gopireddy, D.R., Bandyk, M., Gumus, K.Z. (2025). Radiomics-Based Prediction of Muscle-Invasive Bladder Cancer Using Multi-Parametric MRI. 2025 IEEE 25th International Conference on Bioinformatics and Bioengineering (BIBE). 470-476. IEEE. https://doi.org/10.1109/bibe66822.2025.00084
• Aydin, B., Mete, M., Logue, C., Oruganti, V. (2024). Use of a High Payload Drone and Artificial Intelligence for Low Intensity Fires. ATMAE 2024.
• 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. IEEE. https://doi.org/10.1109/dcas61159.2024.10539910
• 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. IEEE. https://doi.org/10.1109/dcas61159.2024.10539911
• 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. IEEE. https://doi.org/10.1109/bibm52615.2021.9669402
• Mete, M., Arslan, A. (2020). Graphical Processing Unit - Supported RNA Secondary Structure Comparison. Proceedings of the 12th International Conference on Bioinformatics and Computational Biology. 70 109-98. EasyChair. https://doi.org/10.29007/bhsr
• 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. IEEE. https://doi.org/10.1109/bibe50027.2020.00125
• 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. IEEE. https://doi.org/10.1109/icdis.2018.00035
• 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. IEEE. https://doi.org/10.1109/icip.2016.7532993
• 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. 2015 International Conference on Healthcare Informatics. 115-121. IEEE. https://doi.org/10.1109/ichi.2015.21
• Sirakov, N.M., Mete, M., Selvaggi, R., Luong, M. (2015). New Accurate Automated Melanoma Diagnosing Systems. 2015 International Conference on Healthcare Informatics. 374-379. IEEE. https://doi.org/10.1109/ichi.2015.53
• 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.
• 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).
• 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., Sirakov, N.M. (2014). Optimal set of features for accurate skin cancer diagnosis. 2014 IEEE International Conference on Image Processing (ICIP). 2256-2260. IEEE. https://doi.org/10.1109/icip.2014.7025457
• 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.
• Mete, M., Juan Chen, Quan Wen, Xi-Wen Liu. (2012). Color region annotation for microvessel density estimation. 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP). 145-148. IEEE. https://doi.org/10.1109/icwamtip.2012.6413460
• 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.
• Komanduri, R.K., Mete, M. (2012). High Performance Processing of Virtual Slide on GPUs. 9th Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS).
• 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. IEEE. https://doi.org/10.1109/cisp.2012.6469751
• Wen, Q., Wenming Qu, Juan Chen, 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. IEEE. https://doi.org/10.1109/iccps.2012.6384310
• Chen, J., Quan Wen, Zhifei Pang, Mete, M. (2012). An effective approach towards color image segmentation for micro-vessel detection. 2012 International Conference on Computational Problem-Solving (ICCP). 59-63. IEEE. https://doi.org/10.1109/iccps.2012.6384283
• 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. IEEE. https://doi.org/10.1109/cisp.2012.6469669
• 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. IEEE. https://doi.org/10.1109/cisp.2012.6470003
• 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. IEEE. https://doi.org/10.1109/cisp.2012.6469668
• Juan Chen, Quan Wen, Chenglong Zhuo, Mete, M. (2012). Pose recognition of giant pandas based on gradient shapes. 2012 International Conference on Computational Problem-Solving (ICCP). 358-362. IEEE. https://doi.org/10.1109/iccps.2012.6384309
• 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.
• 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.
• 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. IEEE. https://doi.org/10.1109/icip.2011.6115757
• 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..
• 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.
• 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.
• 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. IEEE. https://doi.org/10.1109/asonam.2009.74
• 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. IEEE. https://doi.org/10.1109/isbi.2009.5193097
• 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. IEEE. https://doi.org/10.1109/cibcb.2009.4925740
• 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.
• 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. IEEE. https://doi.org/10.1109/bibm.2007.57
• 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. IEEE. https://doi.org/10.1109/icdmw.2007.73
• 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. IEEE. https://doi.org/10.1109/icdmw.2006.90
• 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. ACM. https://doi.org/10.1145/1066677.1066802

Journal Article

• Toprak, A.C., Mete, M., Shi, J.J., Babushok, D.V., Gurnari, C., Maciejewski, J.P., Bass, Z., Tombul, Z., Santos, C.I., Duran, M.N., Awada, H., Ibrahim, I., Olcal, A., Ozcan, Y., Guerrero, L., Thiebaud, J.A., Bat, T. (2025). Predicting secondary myeloid neoplasms in acquired aplastic anemia using machine learning models. Other. 2 (4) 100163. Elsevier BV. https://doi.org/10.1016/j.bneo.2025.100163
• 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., Gupta, G., Akalin, E., Molnar, M.Z., Mete, M., Ayvaci, M.U., Doshi, M., Tanriover, B. (2025). “The Final Rule in a Bind: What We Are Learning From Suboptimal Simultaneous Heart-Kidney Outcomes and Potential Solutions to the Problem”: Erratum. Other. 109 (8) e424-e424. Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1097/tp.0000000000005489
• Zhu, M., Sharma, P., Mete, M., Olcal, A., Ibrahim, I., Chen, W., Bat, T. (2025). Absolute Immature Platelet Count: An Accessible Biomarker to Distinguish Aplastic Anemia and Immune Thrombocytopenia. Other. 47 (2) 357-361. Wiley. https://doi.org/10.1111/ijlh.14418
• Gumus, K.Z., Menendez, M., Baerga, C.G., Harmon, I., Kumar, S., Mete, M., Hernandez, M., Ozdemir, S., Yuruk, N., Balaji, K., Gopireddy, D.R. (2025). Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer. Computer Methods and Programs in Biomedicine. 259 108528. Elsevier BV. https://doi.org/10.1016/j.cmpb.2024.108528
• Mete, M., Ayvaci, M.U., Gungor, A.B., Araj, F., Acharya, D., Hippen, B., Cheng, X.S., Molnar, M.Z., Alhamad, T., Akalin, E., Singh, N., Anand, P.M., Gupta, G., Peltz, M., Ariyamuthu, V.K., Qannus, A.A., Mansour, I.S., Emami, M., Pal, V., Tanriover, B. (2025). Predicting Simultaneous Heart Kidney Allocation and Posttransplant Adverse Kidney Outcomes. Other. 11 (1) 152-163. Elsevier BV. https://doi.org/10.1016/j.ekir.2025.10.005
• 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 (Supplement 1) 2700-2700. American Society of Hematology. https://doi.org/10.1182/blood-2024-210892
• Duran, M.N., Tombul, Z., Mete, M., Toprak, A.C., Ogbue, O., Awada, H., Gurnari, C., Maciejewski, J., Ibrahim, I.F., Ozturk, M., Ozcan, Y.M., Johnson, B., Ejiofor, C., Olcal, A., Bat, T. (2024). Predicting Extravascular Hemolysis in Paroxysmal Nocturnal Hemoglobinuria. Blood. 144 (Supplement 1) 2695-2695. American Society of Hematology. https://doi.org/10.1182/blood-2024-204213
• 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 (Supplement 1) 1315-1315. American Society of Hematology. https://doi.org/10.1182/blood-2024-203826
• Rajabioun, R., Afshar, M., Atan, Ö., Mete, M., Akin, B. (2024). Classification of Distributed Bearing Faults Using a Novel Sensory Board and Deep Learning Networks With Hybrid Inputs. Other. 39 (2) 963-973. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/tec.2023.3338447
• Abdallah, J., Williams, R.G., Awada, H., Raman, G., Ozcan, Y., Orland, M., Mete, M., Chen, W., Gurnari, C., Maciejewski, J.P., Bat, T. (2024). Thrombocytosis and megakaryocyte changes associated with PRCA. Blood Advances. 8 (12) 3058-3062. American Society of Hematology. https://doi.org/10.1182/bloodadvances.2023012309
• Gumus, K.Z., Contreras, S.S., Al‐Toubat, M., Harmon, I., Hernandez, M., Ozdemir, S., Kumar, S., Yuruk, N., Mete, M., Balaji, K.C., Bandyk, M., Gopireddy, D.R. (2024). MRI‐based radiomic features for identifying recurrent prostate cancer after proton radiation therapy. Other. 25 (3) Wiley. https://doi.org/10.1002/acm2.14293
• 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.
• 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!.
• Calimano-Ramirez, L.F., Virarkar, M.K., Hernandez, M., Ozdemir, S., Kumar, S., Gopireddy, D.R., Lall, C., Balaji, K.C., 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 Science and Business Media LLC. https://doi.org/10.1007/s00261-023-03924-y
• Raman, G., Ashraf, B., Demir, Y.K., Kershaw, C.D., Cheruku, S., Atis, M., Atis, A., Atar, M., Chen, W., Ibrahim, I., Bat, T., Mete, M. (2023). Machine learning prediction for COVID-19 disease severity at hospital admission. Other. 23 (1) Springer Science and Business Media LLC. https://doi.org/10.1186/s12911-023-02132-4
• 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.
• 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., Morlend, R., Drazner, M.H., AbdulRahim, N., Kim, Y., Salam, Y., Gungor, A.B., Delibasi, B., Kotla, S.K., MacConmara, M.P., Mohan Anand, P., Gupta, G., Tanriover, B. (2022). Predicting Post-Heart Transplant Composite Renal Outcome Risk in Adults: A Machine Learning Decision Tool. Other. 7 (6) 1410-1415. Elsevier BV. https://doi.org/10.1016/j.ekir.2022.04.004
• Akar, M., Sirakov, N.M., Mete, M. (2022). Clifford algebra multivectors and kernels for melanoma classification. Other. 45 (7) 4056-4068. Wiley. https://doi.org/10.1002/mma.8034
• 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) Wiley. https://doi.org/10.1111/ctr.13651
• 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. Wiley. https://doi.org/10.1002/jnr.24421
• 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 (S2) Springer Science and Business Media LLC. https://doi.org/10.1186/s12859-019-2625-8
• Ariyamuthu, V.K., Amin, A.A., Drazner, M.H., Araj, F., Mammen, P.P., Ayvaci, M., Mete, M., Ozay, F., Ghanta, M., Mohan, S., Mohan, P., Tanriover, B. (2018). Induction regimen and survival in simultaneous heart-kidney transplant recipients. Other. 37 (5) 587-595. Elsevier BV. https://doi.org/10.1016/j.healun.2017.11.012
• 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., Ariyamuthu, V.K., Hwang, C., Mohan, S., Mete, M., Vazquez, M.A., Marrero, J.A. (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 BV. https://doi.org/10.1016/j.ekir.2016.07.008
• 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 (S13) Springer Science and Business Media LLC. https://doi.org/10.1186/s12859-016-1218-z
• Lemon, J., Kockara, S., Halic, T., Mete, M. (2015). Density-based parallel skin lesion border detection with webCL. BMC Bioinformatics. 16 (S13) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-16-s13-s5
• 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.
• Sirakov, N.M., Ou, Y., Mete, M. (2015). Skin lesion feature vectors classification in models of a Riemannian manifold. Other. 75 (1-2) 217-229. Springer Science and Business Media LLC. https://doi.org/10.1007/s10472-014-9424-8
• Akgün, D., Sakoğlu, Ü., 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. Elsevier BV. https://doi.org/10.1016/j.compmedimag.2015.02.009
• Tanriover, B., Zhang, S., MacConmara, M., Gao, A., Sandikci, B., Ayvaci, M.U., Mete, M., Tsapepas, D., Rajora, N., Mohan, P., Lakhia, R., Lu, C.Y., Vazquez, M. (2015). Induction Therapies in Live Donor Kidney Transplantation on Tacrolimus and Mycophenolate With or Without Steroid Maintenance. Other. 10 (6) 1041-1049. Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.2215/cjn.08710814
• 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.
• 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. Elsevier BV. https://doi.org/10.1016/j.compmedimag.2012.06.002
• Suer, S., Kockara, S., Mete, M. (2011). An improved border detection in dermoscopy images for density based clustering. BMC Bioinformatics. 12 (S10) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-12-s10-s12
• 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.
• Nuthakki, M.K., Mete, M., Varol, C., Suh, S.C. (2011). UXSOM. Other. 36 (3) 1-6. Association for Computing Machinery (ACM). https://doi.org/10.1145/1968587.1968609
• Mete, M., Kockara, S., Aydin, K. (2011). Fast density-based lesion detection in dermoscopy images. Computerized Medical Imaging and Graphics. 35 (2) 128-136. Elsevier BV. https://doi.org/10.1016/j.compmedimag.2010.07.007
• 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.
• 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.
• 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 (S6) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-11-s6-s26
• Mete, M., Sirakov, N.M. (2010). Lesion detection in demoscopy images with novel density-based and active contour approaches. BMC Bioinformatics. 11 (S6) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-11-s6-s23
• 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 (OUP). https://doi.org/10.1093/bioinformatics/btq178
• Mete, M., Tang, F., Xu, X., Yuruk, N. (2008). A structural approach for finding functional modules from large biological networks. BMC Bioinformatics. 9 (S9) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-9-s9-s19
• Mete, M., Xu, X., Fan, C., Shafirstein, G. (2007). Automatic delineation of malignancy in histopathological head and neck slides. BMC Bioinformatics. 8 (S7) Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-8-s7-s17

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.