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Interface Focus|February 9, 2016
Patient-specific blood rheology in sickle-cell anaemiaXuejin Li, E Du, Huan Lei, et al.IEEE Transactions on Neural Networks and Learning Systems|May 26, 2025
Safe Physics-Informed Machine Learning for Optimal Predefined-Time Stabilization: A Lyapunov-Based ApproachNick-Marios T Kokolakis, Zhen Zhang, Shanqing Liu, et al.NPJ Digital Medicine|July 15, 2021
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patientsYixiang Deng, Lu Lu, Laura Aponte, et al.Biophysical Journal|August 20, 2020
Quantifying Fibrinogen-Dependent Aggregation of Red Blood Cells in Type 2 Diabetes MellitusYixiang Deng, Dimitrios P Papageorgiou, Xuejin Li, et al.Journal of the Royal Society, Interface|February 9, 2022
Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator-regression neural networkMinglang Yin, Ehsan Ban, Bruno V Rego, et al.Biorxiv : the Preprint Server for Biology|December 23, 2024
In silico biophysics and rheology of blood and red blood cells in Gaucher DiseaseZhaojie Chai, Guansheng Li, Papa Alioune Ndour, et al.Biophysical Journal|September 19, 2018
Quantifying Platelet Margination in Diabetic Blood FlowHung-Yu Chang, Alireza Yazdani, Xuejin Li, et al.Plos Computational Biology|September 10, 2025
In silico biophysics and rheology of blood and red blood cells in Gaucher DiseaseZhaojie Chai, Guansheng Li, Papa Alioune Ndour, et al.Frontiers in Genetics|October 14, 2024
ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentationMelih Agraz, Dincer Goksuluk, Peng Zhang, et al.Biophysical Journal|January 8, 2019
Quantifying Shear-Induced Deformation and Detachment of Individual Adherent Sickle Red Blood CellsYixiang Deng, Dimitrios P Papageorgiou, Hung-Yu Chang, et al.Pageof 18