• Prof. Dr. Sher Afzal Khan

  • Experience

    Designation Service Type From To
    Professor BPS Aug 18, 2014 Dec 23, 2020

  • Biblography

    .

  • Research Publications

    S.No: Title
    1 SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
    2 SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
    3 iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC
    4 TOWARDS THE SAFETY PROPERTIES OF MOVING BLOCK RAILWAY INTERLOCKING SYSTEM
    5 iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition
    6 Improving moving block railway system using fuzzy multi-agent specification language
    7 NMyristoylG-PseAAC: Sequence-Based Prediction of N-Myristoyl Glycine Sites in Proteins by Integration of PseAAC and Statistical Moments
    8 pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments
    9 Pattern Recognition in Bioinformatics
    10 Software component selection based on quality criteria using the analytic network process.
    11 Portfolio Cost Management In The Context Of Offshore Software Development Outsourcing Relationships From Vendor’s Perspective
    12 A Prediction Model for Membrane Proteins Using Moments Based Features
    13 An Evaluation Framework and Comparative Analysis of the Widely Used First Programming Languages
    14 iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition
    15 SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
    16 Prediction of Nitrosocysteine Sites Using Position and Composition Variant Features
    17 A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.
    18 Correction to: A Two-Layer Computational Model for Discrimination of Enhancer and Their Types Using Hybrid Features Space of Pseudo K-Tuple Nucleotide Composition
    19 iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC. Molecular biology reports
    20 pNitro-Tyr-PseAAC: Predict nitrotyrosine sites in proteins by incorporating five features into Chou's general PseAAC

  • Award

  • Conferences

    Level Country City Conference Dated
    International Pakistan Mardan International Conference on Computational and Social Sciences (ICCSS) Dec 22, 2013
    International Turkey Rize International Conference on Computational and Social Sciences (ICCSS) Jul 25, 2014

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