Saeed Ahmed
PhD Computer Science and Technology (Bioinformatics)
About Me
(اَلسَّلَامُ عَلَيْكُم)! Hello My name is Saeed Ahmed and I am thrilled to introduce myself as a postdoctoral researcher in Machine learning and Bioinformatics. I received my B.S. degree in telecommunication from the University of Science and Technology Bannu, Pakistan, in 2011, and my M.S. degree in computer science from Abdul Wali Khan University Mardan, Pakistan, in 2016. I completed my Ph.D. degree in Computer Science and Technology from the School of Computer Science and Engineering, Nanjing University of Science and Technology (NJUST), Nanjing, China. I worked as a postdoctoral research fellow at the Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand from June 2021 to February 2022. I served as an Assistant Professor at the School of Systems and Technology - Department of Computer Science, University of Management and Technology (UMT), Lahore, Pakistan for 2 years from September 2021 to September 2023, now I am on leave. Currently, I am working as a postdoctoral researcher in the Protein Structure and Bioinformatics Group, Biomedical Center (BMC), Lund University, Sweden. I am keen on immersing myself in a stimulating research environment, aiming to enhance my existing skills while acquiring new ones to remain current in the dynamic field of bioinformatics. In my current postdoctoral research, I am dedicated to advancing machine learning methods specifically tailored for identifying and classifying pathogenic gain-of-function and loss-of-function mutations across the human genome, with the ultimate goal of contributing to research and clinical applications in this area.
Biography
saeed.ahmed@umt.edu.pk
saeed.ahmad075@gmail.com
Urdu (National Language)
Pashto (Mother tongue)
Work Experience
Education
Research Interest
Research Publications
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
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
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
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
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
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
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
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)
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
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 - 104458
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.
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.
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.
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).
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
MS/PhD Thesis Supervised
Editorial and Reviewer Services
Member of Editorial Board in journal "BMC Bioinformatics", BMC Part of Springer Nature"
- Briefings in Bioinformatics.
- Engineering Applications of Artificial Intelligence.
- Current Opinion in Biomedical Engineering.
- Artificial Intelligence In Medicine.
- Journal of Computational Biology.
- Computers in Biology and Medicine.
- Knowledge-Based Systems.
- Information Science.
- Genomics.
- ACS Omega.
- SN Applied Sciences.
- SAR and QSAR in Environmental Research.
- IEEE Journal of Biomedical and Health Informatics.
- Visual Computing for Industry, Biomedicine, and Art.
- Journal of King Saud University - Computer and Information Sciences.
- IEEE Access.
- AI Open.
- Genes - MDPI.
- Symmetry - MDPI.
- International Conference on Innovative Computing – UMT.
- International Conference on Frontiers of Information Technology - COMSATS.
References
Mauno Vihinen
Professor at Lund University
Professor Mauno Vihinen is well-known for his experience and interest in investigating variations and their effects whether they emerge at molecular levels (DNA, RNA protein), in structural context or in the cellular networks and pathways. He has published numerous original ideas and reports, established standards and guidelines for variations and variation databases and developed tools and performed analyses for the interpretation of variations in several diseases. The major part of his production relates to variations ranging from protein engineering to effects and mechanisms of variations in protein structures, genes and diseases.
Maqsood Hayat
Professor at Abdul Wali Khan University Mardan
Professor Maqsood Hayat is an eminent researcher in the field of Bioinformatics, pattern recognition and image processing. He has published more than 50 papers in different aspects of his fields of interests. His research interests lies in the interface of statistical machine learning, pattern recognition, Evolutionary computing and some emerging data-rich areas such as computational biology and bioinformatics. He has a demonstrated history of working in the higher education industry. Skilled in Research, Matlab, Computer Vision, C, and C++. Strong research professional with a focused in Information retreival and processing.