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.

Moses Owoicho Abah | Bioinformatics in Pharmaceuticals | Best Researcher Award

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Moses Owoicho Abah | Bioinformatics in Pharmaceuticals | Best Researcher Award

Dr. Moses Owoicho Abah at World-Class Research Center “Digital biodesign and personalized healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia, Russia.

Dr. Moses Owoicho Abah is a passionate cancer researcher and molecular oncologist 🧪, currently pursuing a Ph.D. in Oncology/Radiation Therapy 🎓 at I.M. Sechenov First Moscow Medical University. With academic roots in Biochemistry, Bioinformatics, and Medical Biochemistry 🧠, he specializes in RNA sequencing, immunoassays, and biomarker discovery. Dr. Abah has worked across leading institutions in Nigeria and Russia 🇳🇬🇷🇺, contributing to drug discovery, genomics, and personalized oncology. He brings deep expertise in data analysis using Python and R 📊, and his impactful research is published in Scopus-indexed journals 📚. His goal is to revolutionize cancer treatment globally.

Publication Profile 

Google Scholar

Education

Dr. Moses Owoicho Abah’s educational path reflects his unwavering dedication to biomedical sciences and cancer research 🧬. He is currently pursuing a Ph.D. in Oncology/Radiation Therapy (2022–2025) at the Institute for Personalized Oncology, I.M. Sechenov First Moscow Medical University 🇷🇺. He earned his M.Sc in Medical Biochemistry (2020–2022) from the University of Abuja 🇳🇬 and completed a Professional Program in Bioinformatics (2019–2020) at MIPT, Russia. His foundation lies in a B.Sc (Hons) in Biochemistry (2010–2015) from FUAM and a Senior School Certificate from GSS Otukpo (2002–2007) 📚. His journey blends science, research, and innovation.

Experience

Since February 2025, Dr. Moses Owoicho Abah has served as a Cancer Research Coordinator at the Genetics, Genomics, and Bioinformatics Department of the National Institute for Cancer Research and Treatment, Nigeria 🇳🇬. He collaborates closely with the Principal Investigator to uphold global research standards 🌍, prepares proposals, budgets, protocols 📄, and handles informed consent and participant screening ✅. Skilled in RStudio and Python 📊🐍, he generates complex data visuals, manages study supplies, and maintains communication with stakeholders. He also revises and submits manuscripts to top journals 📚, contributing meaningfully to global cancer research.

Awards

Dr. Moses Owoicho Abah has received multiple prestigious awards for his excellence in research and academics 🎓. In July 2021, he was honored with the Associate Fellow Award (CMRf-Bioinformatics) by the Royal Society of Clinical and Academic Researchers of Nigeria (ROSCARON) 🧬. In February 2020, he emerged Overall Best Scholar in Clinical Medicine, Chemistry & Biology during the Russian Open Door Scholarship competition 🇷🇺. Earlier, in August 2018, he was conferred an Honorary Fellow Award by the Association of Economists and Statisticians of Nigeria (AESN) 📈—a testament to his cross-disciplinary brilliance.

Research Focus 

Dr. Moses Owoicho Abah’s research lies at the intersection of oncology, molecular medicine, and bioinformatics 🧪. His work focuses on cancer biology, particularly angiogenesis, cytokine signaling, and drug resistance mechanisms in lung and renal cancers 💉🩸. He explores innovative therapies targeting biomarkers using comparative transcriptomics and RNA sequencing for precision medicine. Additionally, he investigates the toxicological effects of everyday compounds, such as triclosan 🧼, and their impact on hormonal balance. His studies also include natural product pharmacology and the anti-diabetic potential of medicinal plants 🌿, bridging traditional medicine with modern therapeutic strategies.

Publication Top Notes

Zhipeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

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Zhipeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award  

Mr. Zhipeng Chen, Jiangsu College of Nursing, China

Mr. Zhipeng Chen is a distinguished academic with a strong foundation in engineering and material sciences. He has led multiple research projects, contributed over 25 articles to SCI and Scopus-indexed journals 📚, and holds several patents under review 🔬. With consultancy roles in over 3 industry-sponsored projects and 2 published books (ISBN) 📖, he plays an active role in applied science. He serves on editorial boards of reputed journals ✍️ and collaborates internationally. A member of prestigious scientific societies 🌐, his research focuses on nanomaterials, biomaterials, and sustainable technologies. His innovations continue to impact academia and industry.

Publication Profile

Orcid

Education

Mr. Zhipeng Chen 🎓 holds advanced degrees in Materials Science and Engineering, demonstrating a strong foundation in cutting-edge technological innovation. With years of professional experience in nanomaterials and sustainable energy solutions ⚙️🌱, he has contributed significantly to academic research through numerous peer-reviewed publications 📚. Mr. Chen has collaborated on international projects, showcasing his ability to bridge theory and practical application 🌍. He has received recognition for his innovative approaches to energy storage and material synthesis 🏆. His commitment to academic excellence and interdisciplinary collaboration continues to drive impactful developments in the field of materials science and engineering.

Professional Memberships

Mr. Zhipeng Chen 🤝 is an active member of several prestigious professional organizations that reflect his commitment to excellence in materials science and engineering. He is a proud member of the Materials Research Society (MRS) 🧪, where he actively participates in conferences and knowledge-sharing events. He is also affiliated with the Institute of Electrical and Electronics Engineers (IEEE) ⚡, contributing to advancements in nanotechnology and energy systems. Additionally, Mr. Chen holds membership in the American Chemical Society (ACS) 🧬, further showcasing his interdisciplinary expertise. These affiliations strengthen his global network and support his continuous professional development 🌐📈.

Research Focus

Mr. Zhipeng Chen focuses his research on geriatric health, specifically exploring the intersection of dynapenic abdominal obesity (DAO) and chronic kidney disease (CKD) in aging populations. His work investigates how the combined impact of reduced muscle strength 💪 and abdominal fat accumulation 🥓 contributes to kidney dysfunction 🚰 in middle-aged and older adults, providing crucial insights into age-related comorbidities. By leveraging data from large-scale cohort studies 📊, Chen aims to support early detection and prevention strategies for CKD in vulnerable populations. His research sits at the crossroads of gerontology, nephrology, and public health 🏥.

Publication Top Notes

Dynapenic abdominal obesity and chronic kidney disease: Results from a nationwide prospective cohort study of middle-aged and older adults in China

Pranab Das | Bioinformatics in Pharmaceuticals | Best Scholar Award

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Pranab Das | Bioinformatics in Pharmaceuticals | Best Scholar Award

Mr Pranab Das, NIT Nagaland, India

Pranab Das is an Assistant Professor in the Department of Computer Science at Kumar Bhaskar Varma Sanskrit and Ancient Studies University, Assam. He holds an M.Tech in Computer Science from NIT Meghalaya and has submitted his Ph.D. thesis at NIT Nagaland. His research focuses on computational biology, drug discovery, and machine learning 🧬🤖. With multiple SCIE and SCI-indexed publications in top journals, he has made significant contributions to predicting drug functions and adverse reactions. Additionally, he has presented at international conferences and authored book chapters 📚. He has qualified JRF, NET, and SLET, showcasing his academic excellence.

Publication Profile

orcid

Education

Pranab Das is a dedicated researcher in Computer Science and Engineering (CSE), currently awaiting the completion of his Ph.D. (2021-2024) from the National Institute of Technology (NIT) Nagaland, where he achieved an impressive 9.23 CGPA 🎓. He holds an M.Tech in CSE (2019-2021) from NIT Meghalaya with a 9.27 CGPA 🏆 and a BE in CSE (2015-2018) from Jorhat Engineering College, securing 73.89% 📚. His academic journey began with a Diploma (70%) from Assam Engineering Institute and 10th grade (66.17%) from Barnibari Milan High School. Additionally, he has cleared JRF, NET, and SLET 🔬✅.

Project Work

Pranab Das is an accomplished researcher specializing in computational biology and drug discovery 🔬💊. His latest work, “MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions”, is published in IEEE Transactions on Computational Biology and Bioinformatics (2025) 📄. He has also co-authored “BRMCF: Binary Relevance and MLSMOTE-Based Computational Framework” (2022) and “Advances in Predicting Drug Functions” (2023). His research extends to machine learning applications in pharmacology, including drug classification and side-effect prediction 🧠📊. With publications in high-impact journals (IF: 2.4-23.8) 📈, Pranab contributes significantly to AI-driven drug discovery 🚀.

Area of Intrest

Pranab Das is deeply passionate about cutting-edge technologies in Artificial Intelligence (AI) 🤖, Machine Learning (ML) 📊, and Deep Learning (DL) 🧠, with a strong focus on their applications in Bioinformatics 🔬. His research explores innovative AI-driven solutions for drug discovery, predictive modeling, and computational biology. By leveraging ML and DL techniques, he aims to enhance drug function prediction, side-effect analysis, and disease modeling. His interdisciplinary approach bridges technology and life sciences, driving impactful advancements in healthcare and pharmaceuticals 💊. Pranab continues to push the boundaries of AI in biomedical research, shaping the future of intelligent drug discovery 🚀.

Research Focus

Pranab Das is a researcher specializing in computational biology, bioinformatics, and artificial intelligence in drug discovery 🧬💻. His work primarily focuses on predicting drug functions, adverse drug reactions, and drug-disease associations using multi-label machine learning and deep neural networks 🤖🧪. He has contributed extensively to the field through innovative frameworks like MLCNNF, K1K2NN, and BRMCF, integrating chemical structures, gene ontology, and biological properties for enhanced drug prediction accuracy 🏥🔬. His research advances COVID-19 drug safety, pharmacology, and computational chemistry, making significant contributions to the intersection of AI and healthcare 🏥📊.

Publication Top Notes

MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions From the Chemical Structure

A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures

BRMCF : Binary Relevance and MLSMOTE based Computational Framework to Predict Drug Functions from Chemical and Biological Properties of Drugs.

A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs

Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short‐term memory

K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects

Advances in Predicting Drug Functions: A Decade-Long Survey in Drug Discovery Research

An Extensive Survey on the Use of Supervised Machine Learning Techniques in the Past Two Decades for Prediction of Drug Side Effects

MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer

Kanwaljit Rana | Bioinformatics in Pharmaceuticals | Best Researcher Award

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Kanwaljit Rana | Bioinformatics in Pharmaceuticals | Best Researcher Award

Dr Kanwaljit Rana,Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, India, India

Dr. Kanwaljit Rana 🧬📊 is a health researcher specializing in computational biology and big data analytics for disease diagnosis and health outcome prediction. Holding a Ph.D. in Biotechnology from GADVASU, India (2022), Dr. Rana integrates large-scale clinical, biological, and omics data using machine learning for precision medicine. With research experience at ICGEB and PGIMER, he has developed bioinformatics pipelines for genomics and predictive health analytics. His expertise spans AI-driven diagnostics, wearable health tech, and big data automation. A published scientist and GATE-qualified scholar, Dr. Rana continues to innovate in health data science and computational medicine. 🚀

Publication Profile

Google scholar

Education

Dr. Kanwaljit Rana 🎓 holds a Ph.D. in Biotechnology from Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), India (2022). With a strong academic foundation, he previously earned a Master’s degree in Biotechnology from HNB Garhwal University, Srinagar. His expertise lies in cutting-edge biotechnological research, contributing to advancements in veterinary and animal sciences 🧬🔬. Passionate about innovation and scientific discovery, Dr. Rana continues to explore new frontiers in biotechnology, striving to make a meaningful impact in the field. His dedication to research and development reflects his commitment to scientific excellence and progressive learning

Professional Experience

Dr. Kanwaljit Rana 🎓 is a dedicated Research Associate at the International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi (Feb 2024 – Aug 2024), where he conducts advanced bioinformatics analysis 🧬, integrating genomics and metagenomics data for disease characterization. Previously, at PGIMER, Chandigarh (Sep 2022 – Jan 2023), he developed automated workflows for tuberculosis meningitis diagnosis 🏥. His research journey includes roles at Punjab Agricultural University (PAU), Ludhiana, and Swami Vivekanand Faculty of Technology & Management, specializing in molecular breeding 🌱 and agricultural biotechnology. He is also a QC Freelancer at Cactus Communications since October 2024 ✍️.

Awards

Dr. Kanwaljit Rana 🎓 is a highly accomplished researcher, having qualified GATE Biotechnology in both 2020 and 2022 🏆, showcasing his expertise in the field. His dedication to innovative research earned him the Best Poster Award 🥇 for his work on “Boon of Cow Curd Consumption” at the National Webinar on Cowpathy and Animal Health (2021) 🐄🧪. His contributions to biotechnology and health sciences continue to make a significant impact, blending traditional knowledge with modern scientific advancements. Dr. Rana remains committed to groundbreaking research and advancements in biotechnology and health sciences 🔬📚

Research Focus

Dr. Kanwaljit Rana’s research primarily focuses on computational genomics 🧬, bioinformatics 🖥️, and veterinary genetics 🐶. His work involves biocomputational analysis of microRNAs, genome assembly, and ancestry studies, particularly in indigenous dog breeds like the Gaddi dog. His expertise extends to genome sequencing, phylogenetics, and the application of programming languages like Python, R, and MATLAB for data analysis. His contributions to veterinary science include genetic insights into animal breeding, evolution, and conservation. Through his interdisciplinary approach, Dr. Rana advances animal genomics, computational biology, and veterinary research, impacting both genetic conservation and bioinformatics applications in animal sciences.

Publication Top Notes

Differential Perspectives Between miRNA and lncRNA in Light of Biogenesis and Functions: A Review

Molecular characterization of indigenous dogs through whole genome analysis

Biocomputational identification of microRNAs from indigenous Gaddi dog genome

Introduction to Python, R, and MATLAB

Qingfeng Chen | Bioinformatics in Pharmaceuticals | Best Researcher Award

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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