Hafiz Hammad | Bioinformatics in Pharmaceuticals | Research Excellence Award

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Mr. Hafiz Hammad | Bioinformatics in Pharmaceuticals | Research Excellence Award

National Centre for Bioinformatics | Pakistan

Mr. Hafiz Hammad is an emerging bioinformaticist and computational biologist whose academic training and professional pursuits reflect a strong interdisciplinary foundation spanning biotechnology, bioinformatics, computer science, artificial intelligence, and data analytics. He is currently pursuing an MPhil in Bioinformatics as a Research Scholar at the Computational Biology Lab, National Center of Bioinformatics, Quaid-i-Azam University, Islamabad. His academic journey includes an Associate Degree in Computer Science from the Virtual University of Pakistan, a BS (Hons) in Biotechnology from the University of the Punjab, Lahore, an FSc. in Pre-Medical from Government College University, Lahore, and his matriculation from Society Public School, Moghalpura, Lahore.Through numerous certifications from internationally recognized institutions—including IBM, Google, Coursera, Novartis, Johns Hopkins University, the University of Toronto, and DTU—Mr. Hammad has developed advanced skills in machine learning, deep learning, data visualization, genomics, pharmacokinetics, cybersecurity, and quantum programming. His technical proficiency is further strengthened by badges in data analysis, data science tools, PyMOL-based molecular visualization, cloud computing, and AI-based enterprise frameworks.Professionally, he has contributed as a Bioinformaticist at BioInfoQuant, a Bioinformatics Analyst at BioInfoXpert, and a Research Apprentice at the University of the Punjab. His practical experience also includes multiple internships in administrative, analytical, and molecular biology settings. Beyond professional roles, he has played a significant part in academic training and capacity building, serving as a facilitator, resource person, and organizer for numerous workshops and national-level training programs on RNA-Seq, NGS data analysis, molecular docking, multi-omics data analysis, and computational biology. His contributions have supported the training of faculty, researchers, and over fifty students across Pakistan.Mr. Hammad has co-authored several peer-reviewed publications, contributing to research in microbiology, drug discovery, structural dynamics, genomics, and computational oncology. His works include Molecular and Metabolic Characterization of Petroleum Hydrocarbon-Degrading Bacillus cereus, Exploring Optimal Drug Targets through Subtractive Proteomics Analysis and Pangenomic Insights for Tailored Drug Design in Tuberculosis, Comprehensive Analysis and Outcomes of Hybridization of Physiologically Active Heterocycles Targeting EGFR, Evaluation of Cannabis-Derived Anti-Inflammatory Treatment and Computational Studies, Genetic Analysis of HPV-16 L1 Gene Mutations and Computational Screening of Therapeutic Inhibitors for Cervical Cancer Treatment, and Identification of Novel Therapeutic Inhibitors against E6 and E7 Oncogenes of HPV-16 Associated with Cervical Cancer.With a rapidly expanding research portfolio, multidisciplinary expertise, and active engagement in scientific training, Mr. Hafiz Hammad continues to establish himself as a promising researcher contributing to advancements in bioinformatics, computational biology, and data-driven biomedical innovation.

Profile: Google Scholar

Featured Publications

Khan MF, Ali A, Rehman HM, Noor Khan S, Hammad HM, Waseem M, et al. Exploring optimal drug targets through subtractive proteomics analysis and pangenomic insights for tailored drug design in tuberculosis. Scientific Reports. 14(1):10904.

Hussain N, Muccee F, Hammad M, Mohiuddin F, Bunny SM, Shahab A. Molecular and metabolic characterization of petroleum hydrocarbons degrading Bacillus cereus. Polish Journal of Microbiology. 73(1):107–120.

Younas S, Nosheen A, Malik ZI, Hussain N, Khan MU, Alhegaili AS, et al. Genetic analysis of HPV-16 L1 gene mutations and computational screening of therapeutic inhibitors for cervical cancer treatment. Medical Oncology. 42(5):153.

Rafiq H, Fareed G, Rehman HM, Yasmeen S, Wu Y, Sohail T, Imran H, et al. Evaluation of cannabis-derived anti-inflammatory and analgesic treatment and identification of cannabinoid-based inhibition of prostaglandin through computational studies. Journal of Biomolecular Structure and Dynamics. 1–14.

Kaur M, Rehman HM, Wu Y, Kaur G, Hammad HM, Usmani YS, Kaur A, et al. Comprehensive analysis and outcomes of hybridization of physiologically active heterocycles targeting epidermal growth factor receptor (EGFR). Computers in Biology and Medicine. 184.

Qingfeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

Published on by Top Pharmaceutical

Qingfeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

Prof Qingfeng Chen, Guangxi University, China

Prof. Qingfeng Chen is a distinguished leader in bioinformatics, data mining, and artificial intelligence. With a Ph.D. in Computer Science from the University of Technology Sydney, he has made significant contributions to drug-target interaction prediction, RNA structure identification, and protein kinase regulation. An active academic leader, he serves as the Chairman of the Guangxi Bioinformatics Association and has held editorial roles in top journals. His extensive publication record and leadership in international conferences reflect his influence. Prof. Chen’s mentorship and dedication to advancing interdisciplinary research make him a trailblazer in his field. 📚💻🔬🌍

Publication Profile

scopus

Educational Backgrounds

Professor Qingfeng Chen holds a Doctor of Philosophy in Computer Science and Technology from the University of Technology Sydney (2004) 🎓. He earned a Master of Mathematics from Guangxi Normal University in 1998, following his Bachelor’s degree in Mathematics from the same institution in 1995 📚. His academic journey reflects a strong foundation in both mathematics and computer science, positioning him as an expert in these fields. With a distinguished academic background, Prof. Chen continues to contribute significantly to the advancement of technology and mathematical research 🔍.

Editorial Role

Prof. Qingfeng Chen is a highly regarded academic, serving as an Associated Editor for Complexity & Intelligent Systems and the Journal of Bioinformatics. He has also contributed as a Guest Editor for notable journals such as Current Protein & Peptide Science, Engineering Letters, and IAENG (International Association of Engineers). Additionally, Prof. Chen serves as a Journal Reviewer for esteemed publications, including Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Information Technology in Biomedicine, Briefing in Bioinformatics, Information Science, Knowledge and Information Systems, and IEEE Transactions on Evolutionary Computation 📚💻🔬.

Current Position

Prof. Qingfeng Chen has held several prestigious positions throughout his career. Since July 2009, he has been a Professor of Bioinformatics, Data Mining, and Artificial Intelligence at the Department of Computer and Electronic Information, Guangxi University. He has also served as an Honorary Research Fellow at La Trobe University in Australia since May 2016. In addition, he is the Chairman of the Guangxi Bioinformatics Association (since August 2022) and the Executive Deputy Director of the Biomedical Informatics Committee of the Guangxi Artificial Intelligence Society (since August 2020). Previously, he was an Honorary Visiting Professor at the University of Technology Sydney and a Research Fellow at City University of Hong Kong 🔬🎓🌍.

Conference Leadership

Prof. Qingfeng Chen has made significant contributions to the academic community, not only through his impactful research and publications but also by showcasing his leadership in organizing key conferences. He has chaired prominent events such as the 12th International Conference on Bioinformatics and Biomedical Science (ICBBS 2023), where his expertise guided discussions on bioinformatics and biomedical advancements. Additionally, he has co-chaired several other international conferences, highlighting his dedication to advancing bioinformatics and artificial intelligence. Through these efforts, Prof. Chen continues to shape and influence the future of these rapidly evolving fields 📅💡🌍.

Extensive Research

Prof. Qingfeng Chen has published a vast array of influential papers and monographs, establishing himself as a leader in bioinformatics. His groundbreaking work in drug-target interaction prediction, RNA structure identification, and protein kinase regulation is highly respected. Notable books such as Intelligent Strategies for Pathway Mining and Secure Transaction Protocol Analysis highlight his expertise in computational biology. Prof. Chen’s journal papers, covering topics like circRNA-disease association prediction and the evolution of SARS-CoV-2, are widely cited and published in prestigious journals like IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Computational Biology and Bioinformatics 📖🔬💡.

Research Focus

Professor Qingfeng Chen’s research primarily focuses on advanced computational biology, particularly in the integration and analysis of multi-omics data for precision medicine. His work involves developing machine learning frameworks, such as interpretable multitask learning models and graph convolutional networks, for predicting cancer outcomes, understanding immune responses, and improving drug-target interaction predictions. His research also explores the application of deep learning techniques to predict responses to therapies like immune checkpoint inhibitors. With a keen interest in cancer biology, his studies aim to enhance biomarker discovery and optimize therapeutic strategies. 🔬💡

Publication Top Notes

scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis

Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma

Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery

IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability

Entity Alignment Based on Dynamic Graph Attention and Label Propagation

NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network