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publications
Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning
Published in PLOS ONE (JCR Q1, IF: 3.7), 2024
This paper presents a hybrid model combining Random Forest and MLP to predict infant behavior from maternal psychological data, achieving an AUC of 0.97.
Recommended citation: Yang, Z., Guo, X., & Huang, J. (2024). "Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning." PLOS ONE. 19(8), e0307332.
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MedSegKAN: A superior medical image segmentation method based on the improved KAN structure
Published in Proceedings of the 16th International Conference on Graphics and Image Processing (ICGIP 2024), 2024
This paper proposes MedSegKAN, an enhanced deep learning architecture for medical image segmentation that achieves a Dice score of 92.89%.
Recommended citation: Fang, Z., Yang, Z., Zhang, X., & Han, Q. (2024). MedSegKAN: A superior medical image segmentation method based on the improved KAN structure， In Proceedings of the 16th International Conference on Graphics and Image Processing (ICGIP).
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Coupled algorithm for investigating microplastics’ impact on fish using unstructured grids
Published in China National Invention Patent, CN 119558222 (Granted Dec. 5, 2025), 2025
This patent protects the novel advection-diffusion coupling algorithm designed to simulate microplastic dynamics and their impact on fish populations using unstructured grids.
Recommended citation: Yang, Z., & Zhang, L. (2025). A coupling algorithm based on unstructured grids to study the impact of microplastics on fish. China National Invention Patent, CN 119558222, Granted Dec. 5, 2025.
Microplastic Transport and Ecological Impacts in the Yangtze River Estuary: A Coupled Modeling Approach
Published in , 2025
This research develops an advection-diffusion coupling algorithm to simulate the migration of microplastics in marine environments. We also improved the Lotka-Volterra model to accurately depict the impact on fish school populations.
Recommended citation: Yang, Z., Zhang, L. (2025). Coupled Modeling Reveals Spatiotemporal Microplastic Dynamics and Ecological Stress in the Yangtze River Estuary.; Manuscript in preparation.
PRISM: A Proteomics Robust Imputation framework for Structure-aware Modeling of missingness
Published in Nature Communications (under review, preprint), 2025
PRISM is a deep learning framework for proteomics data imputation that combines a denoising convolutional autoencoder and deep matrix factorization to model MNAR missingness while preserving biological structure.
Recommended citation: Li, Z., Yang, Z., Chen, Y., & Guo, T. (2025). PRISM: A Proteomics Robust Imputation framework for Structure-aware Modeling of missingness. Nature Communications (under review, preprint).
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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