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

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