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Saeed Ahmad

PhD Computer Science and Technology (Bioinformatics)

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About:

(اَلسَّلَامُ عَلَيْكُم‎)! I earned my PhD in Computer Science and Technology from the School of Computer Science and Engineering at Nanjing University of Science and Technology (NJUST), Nanjing, China, in 2021. During my doctoral studies, I focused on various topics in bioinformatics and protein sequence analysis. Specifically, I conducted research on post-translational modifications and cancer-type prediction and classification using machine and deep learning algorithms. Following my doctoral studies, I served as a postdoctoral researcher at the Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand. During this period, my primary focus was on predicting therapeutic peptides and analyzing their sequences using machine and deep learning algorithms. Additionally, I conducted research on various topics, including the prediction and classification of modification sites in DNA/RNA sequences across different species. Subsequently, I joined the School of Systems and Technology, Department of Computer Science, University of Management and Technology (UMT), Lahore, Pakistan, as an Assistant Professor from September 2021 to September 2023. In this role, I was responsible for teaching, conducting research, and handling administrative duties. During my tenure at UMT, I supervised numerous bachelor's and master's theses covering a wide range of topics, such as the application of machine learning and deep learning in bioinformatics, image processing, computer vision, and data science. Currently, I am serving as a postdoctoral researcher at the Protein Structure and Bioinformatics Group, Biomedical Center (BMC), Lund University, Sweden. I am enthusiastic about immersing myself in a dynamic research environment to enhance my existing skills and acquire new ones in the field of bioinformatics. My current research focuses on developing advanced machine learning techniques tailored for identifying and classifying gain-loss-of-function mutations in the human genome, with the aim of advancing research and clinical applications in this domain. My expertise spans bioinformatics, biomedical imaging, artificial intelligence, deep learning, computer vision, and machine learning. I have hands-on experience with various machine learning and deep learning libraries, including sci-kit-learn, TensorFlow, and PyTorch. Furthermore, I actively participate in research projects related to the explainability of deep neural networks, large language models, image content modification, genetic analysis, and natural language processing using deep learning methodologies.

Biography

  Email

saeed.ahmed@med.lu.se

saeed.ahmad075@gmail.com

saeed.ahmed@umt.edu.pk

  Phone

+46-073-6412410

  Home Address

Buner, Khyber Pakhtunkhwa, Pakistan

  Home Address

Biomedical Center (BMC), Lund University, Sweden.

  Skills

Bioinformatics, Machine Learning, Deep Learning, Image Processing, Biomedical imaging, Databases

  Languages

English (Fluent) Urdu (National Language) Pashto (Mother tongue)

  Collaboration

Open to new collaborations and permanent or long-term opportunities in Bioinformatics Research.

Work Experience:

  1. Researcher  Latest

    Biomedical Center (BMC)
    Lund University, Sweden

  2. Assistant Professor  

    Department of Computer Sciences, SST
    University of Management and Technology (UMT), Lahore, Pakistan

  3. Postdoctoral Research Fellow  

    Center of Data Mining and Biomedical Informatics
    Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.

Education:

  1. PhD in Computer Science and Technology  

    from the School of Computer Science & Engineering
    Nanjing University of Science and Technology, Nanjing, China

    Dissertation: Research on Prediction of Protein Phosphorylation and Cancer Subtypes via Intelligent Computation.

  2. MS Computer Science  

    from Department of Computer Science
    Abdul Wali Khan University Mardan, KP, Pakistan.

    Thesis: Identification of Heat Shock Protein Families and J-Protein Types using Dipeptide Composition and Support Vector Machine.

  3. Bachelor of Science in Telecommunication  

    from Institute of Engineering and Computing Sciences (IECS)
    University of Science and Technology, Bannu, KhyberPakhtunKhawa, Pakistan.

Research Interests:

  1. Machine Learning and Deep Learning

  2. Bioinformatics

  3. Biomedical Imaging

  4. Data Mining and Pattern recognition

Research Publications:

  1. Publications  2024

    1. A Amjad, S Ahmed,M Arif, T Alam, M Kabir*; A novel deep learning identifier for promoters and their strength using heterogeneous features, Methods, Volume 230, October 2024, Pages 119-128.

    2. M A Arshed, S Mumtaz, Stefan C Gherghina, N Urooj, S Ahmed, and C Dewi*; A Deep Learning Model for Detecting Fake Medical Images to Mitigate Financial Insurance Fraud, Computation 2024, 12(9), 173.

    3. R Arif, S Ahmed,S Kanwal, M Kabir*; A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features. [J] Interdisciplinary Sciences: Computational Life Sciences , 2024, 1-16.

    4. F Arshad, A Amjad, S Ahmed, M Kabir*; An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides. [J] Analytical Biochemistry , 2024, 115546.

    5. S Kanwal,R Arif, S Ahmed , M Kabir*; A novel stacking-based predictor for accurate prediction of antimicrobial peptides. [J] Journal of Biomolecular Structure and Dynamics, Taylor & Francis , 2024, 1-12

    6. M A Arshed, H A Rehman, S Ahmed , C Dewi, and H J Christanto; A 16 × 16 Patch-Based Deep Learning Model for the Early Prognosis of Monkeypox from Skin Color Images. [J] Computation - MDPI, 2024, 12(2), 33

    7. M. A. Arshed, S. Mumtaz,M. Ibrahim,C. Dewi ,M. Tanveer and S Ahmed; Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model. [J] Computers- MDPI , 2024, 13(1), 31

  2. Publications  2023

    1. Mehwish Gill, Saeed Ahmad ,Muhammad Kabir*, Maqsood Hayat; A Novel Predictor for the Analysis and Prediction of Enhancers and Their Strength via Multi-View Features and Deep Forest. [J] Information - MDPI , 2023, 14(12), 636

    2. M A Arshed, M Ibrahim, S Mumtaz, M Tanveer and S Ahmed ; Chem2Side: A Deep Learning Model with Ensemble Augmentation (Conventional+ Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images. [J] Information - MDPI,14.12(2023): 663

    3. M. A. Arshed., S.Mumtaz, M.Ibrahim, S Ahmed, MTahir ., & M. Shafi; Multi-class skin cancer classification using vision transformer networks and convolutional neural network-based pre-trained models. [J] Information - MDPI ,14(7), 415

    4. M. A. Arshed., S.Mumtaz, M.Ibrahim, S Ahmed , MTahir., & M. Shafi; Multi-class skin cancer classification using vision transformer networks and convolutional neural network-based pre-trained models. [J] Information - MDPI ,14(7), 415

    5. Hina Alam, Muhammad Burhan, Anusha Gillani, Ihtisham ul Haq, Muhammad Asad Arshed, Muhammad Shafi, and Saeed Ahmed ; IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning [J] Wireless Communications and Mobile Computing - Hindawi ,Volume 2023 | Article ID 1175450

  3. Publications  2022

    1. Saeed Ahmed , Muhammad Arif, Muhammad Kabir*, Khaistah Khan, Yaser Daanial Khan; PredAoDP: Accurate identification of antioxidant proteins by fusing different descriptors based on evolutionary information with support vector machine. [J]Chemometrics and Intelligent Laboratory Systems . 2022, 228 - 104623

    2. Phasit Charoenkwan, Saeed Ahmed , Chanin Nantasenamat, Julian MW Quinn, Mohammad Ali Moni, Pietro Lio’, Watshara Shoombuatong*; AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. [J] Scientific reports .Article number: 7697 (2022)

    3. Saeed Ahmad , Phasit Charoenkwan, Julian MW Quinn, Mohammad Ali Moni, Md Mehedi Hasan, Pietro Lio’, Watshara Shoombuatong*; SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. [J] Scientific Reports . Article number: 4106 (2022

    4. Muhammad Arif, Saeed Ahmad , Fang Ge, Muhammad Kabir*, Yaser Daniaal Khan, Dong-Jun Yu*, Maha Thafar; StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach. [J] Chemometrics and Intelligent Laboratory Systems . 2022, 220 - 10445815

  4. Publications  2021

    1. Saeed Ahmad, Muhammad Kabir , Muhammad Arif, Zaheer Ullah Khan, Dong-Jun Yu*; DeepPPSite: A deep learning based model for analysis and prediction of phosphorylation sites using efficient sequence information. [J] Analytical Biochemistry . 2021, 612, 113955.

    2. Muhammad Arif, Muhammad Kabir, Saeed Ahmad , Abid Khan, Fang Ge, Adel Khelifi, Dong-Jun Yu DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies. [J] IEEE/ACM Transactions on Computational Biology and Bioinformatics . 2022, 19(5), page(s) 2749-2759.

  5. Publications  2020

    1. Saeed Ahmad , Muhammad Kabir*, Muhammad Arif, Zakir Ali, Zar Nawab Khan Swati; Prediction of human phosphorylated proteins by extracting multi-perspective discriminative features from the evolutionary profile and physicochemical properties through LFDA. [J] Chemometrics and Intelligent Laboratory Systems . 2020, 203, 104066.

    2. Muhammad Arif, Saeed Ahmad , Farman Ali, Ge Fang, Min Li, Dong-Jun Yu*; TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. [J] Journal of computer-aided molecular design . Volume 34, pages 841–856, (2020).9.

    3. Muhammad Arif, Farman Ali, Saeed Ahmad , Muhammad Kabir, Zakir Ali, Maqsood Hayat*; Pred-BVP-Unb: Fast Prediction of Bacteriophage Virion Proteins Using Un-biased Multi-perspective Properties with Recursive Feature Elimination. [J] Genomics . 2020, 112(2):1565-1574.

    4. Muhammad Kabir*, Muhammad Iqbal, Saeed Ahmad , Maqsood Hayat*; iNR-2L: A two-level sequence-based predictor developed via Chou’s 5-steps rule and general PseAAC for identifying nuclear receptors and their families. [J] Genomics . 2020, 112(1):276-285.

    5. Farman Ali, Muhammad Arif, Zaheer Ullah Khan, Muhammad Kabir,Saeed Ahmad , Dong-Jun Yu*; SDBP-Pred: Prediction of single-stranded and double-stranded DNA-binding proteins by extending consensus sequence and K-segmentation strategies into PSSM. [J] Analytical Biochemistry . 2020, 589:113494.

  6. Publications  2019

    1. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir , Farman Ali, Zakir Ali, Saeed Ahmed , Jianfeng Lu*; Brain Tumor Classification for MR Images using Transfer Learning and Fine-Tuning. [J] Computerized Medical Imaging and Graphics . 2019, 75:34-46.

    2. Farman Ali,Saeed Ahmed , Zar Nawab Khan Swati, Shahid Akbar; DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information. [J] Journal of Computer-Aided Molecular Designs . Volume 33, pages 645–658, (2019)

    3. SM Hasan Mahmud, Wenyu Chen, Hosney Jahan, Yongsheng Liu, Nasir Islam Sujan, Saeed Ahmed ; iDTi-CSsmoteB: identification of drug–target interaction based on drug chemical structure and protein sequence using XGBoost with over-sampling technique SMOTE. [J] IEEE Access . vol. 7, pp. 48699-48714, 2019.

    4. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir, Farman Ali, Zakir Ali, Saeed Ahmed , Jianfeng Lu*; Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning. [J] IEEE Access. 2019, 7(1):17809-17822.

    5. Muhammad Kabir, Muhammad Arif, Farman Ali, Saeed Ahmad , Zar Nawab Khan Swati, Dong-Jun Yu*; Prediction of membrane protein types by exploring local discriminative information from evolutionary profiles. [J] Analytical Biochemistry . 2019, 564-565:123-132.

  7. Publications  2018

    1. Saeed Ahmad* , Muhammad Kabir, Zakir Ali, Muhammad Arif, Farman Ali, Dong-Jun Yu; An Integrated Feature Selection algorithm for Cancer Classification using Gene Expression Data. [J]Combinatorial Chemistry & High Throughput Screening . 2018, 21(9):631-645.

    2. Saeed Ahmad *, Muhammad Kabir*, Muhammad Arif, Zakir Ali, Farman Ali, Zar Nawab Khan Swati; Improving secretory proteins prediction in Mycobacterium tuberculosis using the unbiased dipeptide composition with support vector machine. [J] International Journal of Data Mining and Bioinformatics . 2018,21(3):212-229.

    3. Muhammad Kabir, Muhammad Arif, Saeed Ahmad*, Zakir Ali, Zar Nawab Khan Swati, Dong-Jun Yu*; Intelligent computational method for discrimination of anticancer peptides by incorporating sequential and evolutionary profiles information. [J] Chemometrics and Intelligent Laboratory Systems . 2018, 182:158-165.

    4. Muhammad Kabir, Saeed Ahmad , Muhammad Iqbal, Zar Nawab Khan Swati, Zi Liu, Dong-Jun Yu*; Improving prediction of extracellular matrix proteins using evolutionary information via a grey system model and asymmetric under-sampling technique. [J] Chemometrics and Intelligent Laboratory Systems. 2018, 174; 22-32.

  8. Publications  2017

    1. Muslim Khan, Maqsood Hayat, Sher Afzal Khan, Saeed Ahmad , Nadeem Iqbal; Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins. [J] Journal of Theoretical Biology . 2017, 435; 116-124.

  9. Publications  2015

    1. Muhammad Kabir, Muhammad Iqbal, Saeed Ahmad, Maqsood Hayat*; iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition. [J] Computers in Biology and Medicine . 2015, 66: 252-257.

    2. Saeed Ahmad , Muhammad Kabir, Maqsood Hayat*; Identification of Heat Shock Protein Families and J-Protein Types by incorporating Dipeptide Composition into Chou's general PseAAC. [J] Computer Methods and Programs in Biomedicine. 2015, 122: 165-174.

  10. Conference Papers  

    1. Adil Yousaf, Muhammad Rashid Rasheed, Muhammad Arif, Abdullah Yousafzai, Muhammad Kabir, Saeed Ahmed *; Recent advancements in predicting protein phosphorylation sites using machine learning methods. [C] 2021 International Conference on Innovative Computing (ICIC) -IEEE . 2021, 1-6.

    2. Muhammad Rashid Rasheed, Mehwish Gill, Muhammad Asif Subhani, Muhammad Arif, Saeed Ahmed , Muhammd Kabir; Comprehensive analysis of machine learning based predictors for identifying DNase I hypersensitive site. [C] 2021 International Conference on Innovative Computing (ICIC) -IEEE. 2021, 1-6.

    11. *Corresponding Author.

MS/PhD Thesis Supervised:

  1. Mehwish Gill  Co-Supervisor - 2022

    Prediction and analysis of computational methods for the identification of enhancers and their strength.

  2. Roha Arif  Co-Supervisor - 2023

    Identification of Tumor-Homing Peptides Using Deep features combined Support vector machine.

  3. Sameera Kanwal  Supervisor - 2023

    Prediction of Antimicrobial peptides using feature representation learning.

  4. Abdullah Muhammad Asghar  Supervisor - 2023

    Large-scale comparative review and assessment of computational methods for identification of nucleosome positioning.

  5. Farwa Arshad  Co-Supervisor - 2023

    Identification of anti-diabetic peptides using stacked based ensemble learning.

  6. Aqsa Amjad  Supervisor - 2023

    Analysis and identification of promoters and their strength based on a deep neural network with sequential features.

  7. Shumaila Kanwal  Co-Supervisor - 2023

    Prediction of Ampylation sites using protein language model with deep cascade random forest.

  8. Hina Farooq  Supervisor - 2023

    A novel approach for accurate prediction of Cyclin Proteins using stacking ensemble based learning.

  9. Muhammad Rehan Amjad  Supervisor - 2023

    Prediction of Antiviral Peptides using Long-Short-Term-Memory (LSTM) Based Convolution Neural Network .

  10. ZUBDA KHANUM  Supervisor - 2023

    Analysis on prediction of lysine malonylation sites by exploiting informative features in machine learning framework using principal component analysis.

  11. Zoha Kashaf  Supervisor - 2023

    Cell-Specific Long Non-Coding RNA Prediction Using Deep Learning Techniques.

Editorial and Reviewer Services:

  1. Editorial Services  

    Member of Editorial Board in journal &qout;BMC Bioinformatics&qout;, BMC Part of Springer Nature&qout;

  2. Review Services  

    • 1. Briefings in Bioinformatics.
    • 2. Engineering Applications of Artificial Intelligence.
    • 3. Current Opinion in Biomedical Engineering.
    • 4. Artificial Intelligence In Medicine.
    • 5. Journal of Computational Biology.
    • 6. Computers in Biology and Medicine.
    • 7. Knowledge-Based Systems.
    • 8. Information Science.
    • 9. Genomics.
    • 10. ACS Omega.
    • 11. SN Applied Sciences.
    • 12. SAR and QSAR in Environmental Research.
    • 13. IEEE Journal of Biomedical and Health Informatics.
    • 14. Visual Computing for Industry, Biomedicine, and Art.
    • 15. Journal of King Saud University - Computer and Information Sciences.
    • 16. IEEE Access.
    • 17. AI Open.
    • 18. Genes - MDPI.
    • 19. Symmetry - MDPI.
    • 20. International Conference on Innovative Computing – UMT.
    • 21. International Conference on Frontiers of Information Technology - COMSATS.

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Dr. Saeed Ahmad

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