Zhiwen Yang

Zhiwen Yang

I have a wide range of research interests, including machine learning, deep learning and their application to computational biology, with a focus on developing tools to solve real world problems.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

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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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.

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.

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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Conference Papers

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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