Faculty Profile

Dr Ahmad Hassan Butt

Assistant Professor, HEC Approved Supervisor

School of Systems and Technology

Department of Computer Science

 : [email protected]     : 3880   

Dr. Ahmad Hassan Butt has completed his PhD in Computer Science from University of Management and Technology. He has worked in the software industry for 6 years and has vast experience in C/C++, Java, .NET and Python languages. He has vast experience in VStudio.NET based Compilers/IDE. His areas of research are Pattern Recognition, Machine Learning, Bioinformatics and Computational Genomics and Computational Proteomics. He is also working as an active member of Bioinformatics Research Group in Department of Computer Science at SST-UMT. 

The following are some of his research publications within Impact Factor Journals.

  • Butt, A. H., Khan, S. A., Jamil, H., Rasool, N., & Khan, Y. D. (2016). A prediction model for membrane proteins using moments based features. BioMed research international, 2016. (IF:3.12)
  • Butt, A. H., Mahmood, M. K., & Khan, Y. D. (2016). AN EXPOSITION ANALYSIS OF FACIAL EXPRESSION RECOGNITION TECHNIQUES. Pakistan Journal of Science, 68(3).
  • Butt, A. H., Rasool, N., & Khan, Y. D. (2017). A treatise to computational approaches towards prediction of membrane protein and its subtypes. The Journal of membrane biology, 250(1), 55-76. (IF:2.56)
  • Butt, A. H., Rasool, N., & Khan, Y. D. (2018). Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC. Molecular biology reports, 45(6), 2295-2306. (IF:2.84)
  • Butt, A. H., Rasool, N., & Khan, Y. D. (2019). Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC. Journal of theoretical biology, 473, 1-8. (IF:3.13)
  • Butt, A. H., & Khan, Y. D. (2019). Prediction of S-Sulfenylation sites using statistical moments based features via Chou’S 5-Step rule. International Journal of Peptide Research and Therapeutics, 1-11. (IF:1.55)
  • Butt, A. H., & Khan, Y. D. (2019). CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences. IEEE Access, 8, 9520-9531. (IF:4.098)

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