Dr. Iman Dehzangi

Title: 
Assistant Professor of Computer Science
Office Location: 
McMechen Hall 620
Email: 
Abdollah.Dehzangi@morgan.edu
Education:

Ph.D. Griffith University, Brisbane, Australia, 2015
M.S. MultiMedia University, Cyberjaya, Malaysia, 2011
B.S. Shiraz University, Shiraz, Iran, 2007

Education:

Ph.D. Griffith University, Brisbane, Australia, 2015
M.S. MultiMedia University, Cyberjaya, Malaysia, 2011
B.S. Shiraz University, Shiraz, Iran, 2007

Research Interest:

Dr. Abdollah Dehzangi joined as an Assistant Professor in the Computer Science Department at Morgan State University from Fall, 2017. He received his PhD in Bioinformatics and Computational biology from the Griffith University, Brisbane, Australia in 2015. During his PhD, he also served as Research Scholar at National ICT Australia (NICTA) from 2011 to 2014. After obtaining his PhD, he served as Research scholar at Griffith University (2014-2015) and later as a Post Doctoral Research Scholar at University of Iowa (2015-2017).

His research focuses on Bioinformatics & Computational Biology, and Machine learning in general and designing and developing tools for protein local and global structure prediction, Genome variants and their impact on psychiatric diseases, deep learning as well as ensemble architecture and their application in protein function prediction problems. He is interested in the challenges associated with the design and development of robust, general, and accurate systems to predict the local and global structure of the proteins as the critical step towards protein function prediction, as well as systems to determine the spatiotemporal impact of coding and non-coding variants on different brain regions and their associations with psychiatric diseases. He is also interested to tackle these challenges using Machine learning techniques with emphasis on deep neural network, support vector machines, and ensemble classifiers (homogeneous and heterogeneous).

Dr. Dehzangi has published around 50 articles in refereed journals and conference proceedings, such as Bioinformatics, Scientific Report, PloS One, IEEE Transaction on Computational Biology and Bioinformatics, BMC Bioinformatics, BMC Genomics, Journal of Computational Chemistry, IEEE Transaction on NanoBioScience, Journal of Theoretical Biology etc. He also presented his works in several conferences and workshops, such as PRIB, ICONIP, IncoB, APBC, and ACIIDS over the years.

Additional Links:

Google Scholar:
https://scholar.google.com/citations?user=RkamSRYAAAAJ&hl=en

Research Gate:
https://www.researchgate.net/profile/Iman_abdollah_Dehzangi

Selected Publications (Total: 51, h-Index 15, I-index =24, citations: 710):

  • Dehzangi, A., López, Y., Lal, S.P., Taherzadeh, G., Michaelson, J., Sattar, A., Tsunoda, T. and Sharma, A., PSSM-Suc: Accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction. Journal of Theoretical Biology, 425, pp.97-102, 2017.
  • Dehzangi, A., López, Y., Lal, S.P., Taherzadeh, G., Michaelson, J., Sattar, A., Tsunoda, T. and Sharma, A., SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids. Analytical Biochemistry, 527, pp.24-32, 2017.
  • Heffernan, R., Dehzangi, A., Lyons, J., Paliwal, K., Sharma, A., Wang, J., Sattar, A., Zhou, Y. and Yang, Y., Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins. Bioinformatics, p.btv665, 2015.
  • Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K. and Sattar, A., Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳ s general PseAAC. Journal of theoretical biology, 364, pp.284-294, 2015.
  • Heffernan, R., Paliwal, K., Lyons, J., Dehzangi, A., Sharma, A., Wang, J., Sattar, A., Yang, Y. and Zhou, Y., Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Scientific reports, 5, 2015.
  • Dehzangi, A., Paliwal, K., Lyons, J., Sharma, A. and Sattar, A., Proposing a highly accurate protein structural class predictor using segmentation-based features. BMC genomics, 15(1), p.S2, 2014.