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Xin Gao

Professor, Computer Science | Chair, Computer Science Program | Co-Chair, Center of Excellence for Smart Health

Computer, Electrical and Mathematical Science and Engineering Division 
Center membership :
Computational Bioscience

xin.gao@kaust.edu.sa
Structural and Functional Bioinformatics Group


Affiliations

Education Profile

  • Ph.D. University of Waterloo, Canada, 2009
  • B.S. Tsinghua University, 2004

Research Interests

Dr. Gao is a world-renowned expert on developing AI solutions for diagnostics of genetic diseases and cancers, drug development, and biomedical imaging. His research interest lies at the intersection between AI and biology. His group works on building computational models, developing machine learning techniques, and designing efficient and effective algorithms to tackle key open problems along the path from biological sequence analysis, to 3D structure determination, to function annotation, to understanding and controlling molecular behaviors in complex biological networks, and to biomedicine and healthcare.

Selected Publications

  • Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, and Xin Gao*. (2024). “Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4”. Nature Communications. 15: 5649.
  • Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Chencheng Xu, Di Wang, and Xin Gao*. (2023). “PPML-Omics: a privacy-preserving federated machine learning method protects patients’ privacy from omic data”. Science Advances. 10: eadh8601.
  • Bin Zhang, and Xin Gao*. (2023). “Deciphering DNA variant-associated aberrant splicing with the aid of RNA sequencing”. Nature Genetics. 55: 732-733.
  • Longxi Zhou, Xianglin Meng, Yuxin Huang, Kai Kang, Juexiao Zhou, Yuetan Chu, Haoyang Li, Dexuan Xie, Jiannan Zhang, Weizhen Yang, Na Bai, Yi Zhao, Mingyan Zhao, Guohua Wang, Lawrence Carin, Xigang Xiao, Kaijiang Yu, Zhaowen Qiu, and Xin Gao*. (2022). “An Interpretable deep learning workflow for discovering sub-visual abnormalities in CT scans of COVID-19 inpatients and survivors”. Nature Machine Intelligence. 4: 494-503.
  • Longxi Zhou, Zhongxiao Li, Juexiao Zhou, Haoyang Li, Yupeng Chen, Yuxin Huang, Dexuan Xie, Lintao Zhao, Ming Fan, Shahrukh Hashmi, Faisal AbdelKareem, Riham Eiada, Xigang Xiao*, Lihua Li*, Zhaowen Qiu*, and Xin Gao*. (2020). “A rapid, accurate and machine-agnostic segmentation and quantification method for CT-based COVID-19 diagnosis”. IEEE Transactions on Medical Imaging. 39(8): 2638-2652.
  • Yu Li, Sheng Wang, Ramzan Umarov, Bingqing Xie, Ming Fan, Lihua Li, and Xin Gao*. (2018). “DEEPre: sequence-based enzyme EC number prediction by deep learning”. Bioinformatics. 34(5): 760-769.