PHD at the University of Technology Sydney, Australia
Professor Qingfeng Chen is a distinguished scholar in bioinformatics, data mining, and artificial intelligence at Guangxi University. Holding a Ph.D. from the University of Technology Sydney, he also serves as an honorary research fellow at La Trobe University, Australia. Dr. Chen’s leadership includes roles as Chairman of the Guangxi Bioinformatics Association and Executive Deputy Director of the Biomedical Informatics Committee of Guangxi Artificial Intelligence Society. He has chaired numerous international conferences and serves on editorial boards for prominent journals. His research spans computational biology, with a focus on developing advanced algorithms for biomedical data analysis and application of artificial intelligence in healthcare.
Professional Profiles
Education
Doctor of Philosophy in Computer Science and Technology University of Technology Sydney, 2004 Master of Mathematics Guangxi Normal University, 1998 Bachelor of Mathematics Guangxi Normal University, 1995
Current Positiions
Professor of Bioinformatics, Data Mining and Artificial Intelligence Research Department Guangxi University, July 2009 – Present Honorary Research Fellow La Trobe University, Australia, May 2016 – Present Chairman Guangxi Bioinformatics Association, August 2022 – Present Executive Deputy Director Biomedical Informatics Committee, Guangxi Artificial Intelligence Society, August 2020 – Present Honorary Visiting Professor University of Technology Sydney, April 2013 – December 2014 Research Fellow City University of Hong Kong, February 2013 – April 2013
Research Focus
Professor Qingfeng Chen’s research focuses on the application of deep learning and advanced computational methods in bioinformatics and biomedical data analysis. His work includes developing models for predicting responses to treatments in hepatocellular carcinoma using multiphase CT images, integrating multi-omics data with transformer and graph convolutional networks, and predicting circRNA-disease associations. Additionally, he explores frameworks like DeepKEGG for cancer recurrence prediction and biomarker discovery, as well as neural network interpretability in lung adenocarcinoma. His contributions also extend to drug-target interaction prediction and entity alignment, showcasing a diverse range of expertise in computational biology and artificial intelligence.
Publications
- LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features, Publication date: 2024.
- Similarity-guided graph contrastive learning for lncRNA-disease association prediction, Publication date: 2024.
- SSLDTI: A novel method for drug-target interaction prediction based on self-supervised learning, Publication date: 2024.
- NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network, Publication date: 2024.
- IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability, Publication date: 2024.
- DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery, Publication date: 2024.
- Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only, Publication date: 2024.
- A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks, Publication date: 2024.
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma, Publication date: 2024.
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