Zobrazeno 1 - 10
of 784
pro vyhledávání: '"An, Bozhen"'
Autor:
Tan, Cheng, Cao, Zhenxiao, Gao, Zhangyang, Wu, Lirong, Li, Siyuan, Huang, Yufei, Xia, Jun, Hu, Bozhen, Li, Stan Z.
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes. Accurately predicting PTM sites and their specific t
Externí odkaz:
http://arxiv.org/abs/2411.01856
Autor:
Liu, Sizhe, Xia, Jun, Zhang, Lecheng, Liu, Yuchen, Liu, Yue, Du, Wenjie, Gao, Zhangyang, Hu, Bozhen, Tan, Cheng, Xiang, Hongxin, Li, Stan Z.
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, the large feasible model space of MRL poses significant challenges to be
Externí odkaz:
http://arxiv.org/abs/2410.15010
Autor:
Li, Zhiwei, Zhang, Bozhen, Yang, Lei, Shen, Tianyu, Xu, Nuo, Hao, Ruosen, Li, Weiting, Yan, Tao, Liu, Huaping
V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current research primarily focuses on enhancing perception accuracy, often ove
Externí odkaz:
http://arxiv.org/abs/2405.03971
Autor:
Zhou, Bozhen, Chen, Shu
Publikováno v:
Phys. Rev. B 110, 064318 (2024)
We study the spread complexity in two-mode Bose-Einstein condensations and unveil that the long-time average of the spread complexity $\overline{C}_{K}$ can probe the dynamical transition from self-trapping to Josephson oscillation. When the paramete
Externí odkaz:
http://arxiv.org/abs/2403.15154
Autor:
Hu, Bozhen, Tan, Cheng, Wu, Lirong, Zheng, Jiangbin, Xia, Jun, Gao, Zhangyang, Liu, Zicheng, Wu, Fandi, Zhang, Guijun, Li, Stan Z.
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes. In recent years, deep learning has emerged as a powerful tool for
Externí odkaz:
http://arxiv.org/abs/2403.05314
Autor:
Wu, Lirong, Huang, Yufei, Tan, Cheng, Gao, Zhangyang, Hu, Bozhen, Lin, Haitao, Liu, Zicheng, Li, Stan Z.
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structu
Externí odkaz:
http://arxiv.org/abs/2402.08198
Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling. Despite recent graph neural networks and graph tra
Externí odkaz:
http://arxiv.org/abs/2402.02464
Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks. These models can capture
Externí odkaz:
http://arxiv.org/abs/2402.09416
Publikováno v:
In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent representations by minimizing reconstruction errors. Rare work considers the da
Externí odkaz:
http://arxiv.org/abs/2401.06727
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities. However, challenges such as low resolution, high noise, and uneven heating in infrared thermal image
Externí odkaz:
http://arxiv.org/abs/2311.10245