Dmitriy Shin, PhD, MSc

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Dmitriy Shin

Assistant Professor
phone: 573-882-6940
email: shindm@health.missouri.edu

Degrees:
Ph.D., Biomedical Informatics, Univeristy of Missouri
M.Sc., Computer Science, Moscow State Academy of Computer Science and Engineering

 

Academic appointments:
Assistant Professor, Translational and Cancer Bioinformatics, Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia

Administrative appointments: Director of Pathology Informatics/IT, Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia

Research Interests:

  • translational bioinformatics
  • computational morphoproteomics
  • computational cancer biology
  • machine learning, data mining and computational intelligence

Research description: Dr. Shin is studying complex computational methods in biomedical domain, including computational morpohoproteomics, digital pathology and computational cancer biology.

Currently he is working on computational methods for determining the identification of antibody panels for the classification of lymphomas that will provide an optimally efficient diagnostic path. This research direction, reffered as "morphoproteomics", is a part of a new emerging subfield of pathology described as"systems pathology". The work is done in collaboration with Drs. Arthur, Shyu, Korkin and Popescu.

Dr. Shin has also been working in other areas of biomedical informatics. He collaborates with Dr. Mariuis Petruc on studying factors that influence the quality of radiology imaging investigations using computational models.  Another collaboration between Dr. Shin and Dr. Petruc is the study of telemedical methods to encourage positive behavioral patterns in type I diabetes pediatric patients, where they are co-principal investigators. The research is funded by the Children's Miracle Network grant. For this study, Dr. Shin has designed a long range wireless transmitter device that allows blood glucose data to be remotely sent to the processing center where they are analyzed using machine learning and computational intelligence methods. The telemedical methods encouraging positive behavior in diabetes patients includes system initiated reminders and alerts that are generated based on complex analysis of blood glucose, Hemoglobin A1c (HbA1c) and other measurements and delivered to patients using cutting edge Text-To-Speech and Voice-over-IP technologies.

Publications:

  • D. Shin*, E. Rogatsky, A. Stoyanov*, "Simultaneous monitoring of multiple transitions in mass spectrometric analysis improves limit of detection for low abundance substances in complex biological samples." J Chromatograph Separat Techniq 4: 206. doi:10.4172/2157- 7064.1000206, 2014
  • Chen Z, Shin D, Chen S, Mikhail K, Hadass O, et al. (2014) Histological Quantitation of Brain Injury Using Whole Slide Imaging: A Pilot Validation Study in Mice. PLoS ONE 9(3): e92133. doi:10.1371/journal.pone.0092133
  • Orr Hadass, Brittany N. Tomlinson, Major Gooyit, Shanyan Chen, Justin J. Purd, Jennifer M. Walker, Chunyang Zhang, Andrew B. Giritharan, Whitley Purnell, Christopher R. Robinson, Dmitriy Shin, Valerie A. Schroeder, Mark A. Suckow, Agnes Simonyi, Grace Y. Sun, Shahriar Mobashery, Jiankun Cui, Mayland Chang*, Zezong Gu*, “Selective Inhibition of Matrix Metalloproteinase-9 Attenuates Secondary Damage Resulting from Severe Traumatic Brain Injury.”, PLOS One, 2013, 8:10
  • D. Shin*, G. Arthur, C. Caldwell, M. Popescu, M. Petruc, A. Diaz-Arias, and C. Shyu, “A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method,” Journal of Pathology Informatics, vol. 3, no. 1, p. 1, 2012.
  • Multi-resolution tile-based follicle detection using color and textural information of follicular lymphoma IHC slides Han, J. Shin, D.V. Arthur, G.L. Shyu, C.R., BIBM, 2010

 

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