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2025

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Computer Science

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41
Citations
3474
World Ranking
9004
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Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yaser Daanial Khan is affiliated with the University of Management and Technology in Pakistan. Their primary research field is Biochemistry, Genetics and Molecular Biology, with a particular focus on Molecular Biology. Their work also touches on Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Epidemiology, and Cancer Research.

The scientist's research covers a range of topics related to bioinformatics and molecular science. Major themes include:

  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Vaccines and immunoinformatics approaches
  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics
  • Genetics, Bioinformatics, and Biomedical Research

Yaser Daanial Khan has published extensively in multiple scientific venues. Notable publication outlets where they have multiple papers include:

  • Scientific Reports (7 publications)
  • Digital Health (5 publications)
  • Current Bioinformatics (4 publications)
  • Analytical Biochemistry (3 publications)
  • IEEE Access (3 publications)

Among recent publications are the following:

  • "Evaluating machine learning methodologies for identification of cancer driver genes," 2021, Scientific Reports
  • "Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification," 2020, Scientific Reports
  • "StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach," 2021, Chemometrics and Intelligent Laboratory Systems
  • "Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations," 2020, Analytical Biochemistry
  • "Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD," 2020, Current Drug Discovery Technologies

The scientist collaborates frequently with several researchers in their domain. Frequent coauthors include:

  • Tamim Alkhalifah
  • Fahad Alturise
  • Waqar Hussain
  • Sharaf J. Malebary
  • Nouman Rasool

Best Publications

  • SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

    Waqar Hussain;Yaser Daanial Khan;Nouman Rasool;Sher Afzal Khan

  • SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.

    Waqar Hussain;Yaser Daanial Khan;Nouman Rasool;Sher Afzal Khan

  • iPhosT-PseAAC: Identify phosphothreonine sites by incorporating sequence statistical moments into PseAAC.

    Yaser Daanial Khan;Nouman Rasool;Waqar Hussain;Sher Afzal Khan

  • Prediction of N-linked glycosylation sites using position relative features and statistical moments.

    Muhammad Aizaz Akmal;Nouman Rasool;Yaser Daanial Khan

  • Iris recognition using image moments and k-means algorithm.

    Yaser Daanial Khan;Sher Afzal Khan;Farooq Ahmad;Saeed Islam

  • iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

    Yaser Daanial Khan;Nouman Rasool;Waqar Hussain;Sher Afzal Khan

  • A Novel Modeling in Mathematical Biology for Classification of Signal Peptides

    Asma Ehsan;Khalid Mahmood;Yaser Daanial Khan;Sher Afzal Khan

  • 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

    Muhammad Awais;Waqar Hussain;Yaser Daanial Khan;Nouman Rasool

  • A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes

    Ahmad Hassan Butt;Nouman Rasool;Yaser Daanial Khan

  • Predicting membrane proteins and their types by extracting various sequence features into Chou’s general PseAAC

    Ahmad Hassan Butt;Nouman Rasool;Yaser Daanial Khan

  • pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC

    Ahmad W Ghauri;Yaser D Khan;Nouman Rasool;Sher A Khan

  • StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach

    Muhammad Arif;Muhammad Arif;Saeed Ahmed;Fang Ge;Muhammad Kabir

  • A Neuro-Cognitive Approach for Iris Recognition Using Back Propagation

    Yaser Daanial Khan;Farooq Ahmad;Muhammad Waqas Anwar

  • A Prediction Model for Membrane Proteins Using Moments Based Features

    Ahmad Hassan Butt;Sher Afzal Khan;Hamza Jamil;Nouman Rasool

  • Evaluating machine learning methodologies for identification of cancer driver genes.

    Sharaf J. Malebary;Yaser Daanial Khan

  • Situation recognition using image moments and recurrent neural networks

    Yaser Daanial Khan;Farooq Ahmed;Sher Afzal Khan

  • iCrotoK-PseAAC: Identify lysine crotonylation sites by blending position relative statistical features according to the Chou's 5-step rule

    Sharaf Jameel Malebary;Muhammad Safi ur Rehman;Yaser Daanial Khan

  • Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification

    Asghar Ali Shah;Yaser Daanial Khan

  • Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC.

    Ahmad Hassan Butt;Nouman Rasool;Yaser Daanial Khan

  • iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components.

    Omar Barukab;Yaser Daanial Khan;Sher Afzal Khan;Kuo Chen Chou

  • iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule.

    Sarah Ilyas;Waqar Hussain;Adeel Ashraf;Yaser Daanial Khan

Frequent Co-Authors

Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Muhammad Farooq
Muhammad Farooq Sultan Qaboos University
Saeed Islam
Saeed Islam Abdul Wali Khan University Mardan
Daojing He
Daojing He East China Normal University
Dong-Jun Yu
Dong-Jun Yu Nanjing University of Science and Technology

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