Zobrazeno 1 - 10
of 548
pro vyhledávání: '"Zhang, Weixiong"'
The task of understanding and interpreting the complex information encoded within genomic sequences remains a grand challenge in biological research and clinical applications. In this context, recent advancements in large language model research have
Externí odkaz:
http://arxiv.org/abs/2409.15697
Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. External control signals can be applied to a designated set of nodes within a network, known as the Minimum Driver Set (MDS), to steer
Externí odkaz:
http://arxiv.org/abs/2302.09743
Autor:
Wei, Xinru, Dong, Shuai, Su, Zhao, Tang, Lili, Zhao, Pengfei, Pan, Chunyu, Wang, Fei, Tang, Yanqing, Zhang, Weixiong, Zhang, Xizhe
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard s
Externí odkaz:
http://arxiv.org/abs/2302.00104
Autor:
Zhang, Zhuangzhuang, Zhang, Weixiong
We developed a novel SSL approach to capture global consistency and pixel-level local consistencies between differently augmented views of the same images to accommodate downstream discriminative and dense predictive tasks. We adopted the teacher-stu
Externí odkaz:
http://arxiv.org/abs/2210.00646
Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolution
Externí odkaz:
http://arxiv.org/abs/2206.13953
Autor:
Zhang, Xizhe, Pan, Chunyu, Wei, Xinru, Yu, Meng, Liu, Shuangjie, An, Jun, Yang, Jieping, Wei, Baojun, Hao, Wenjun, Yao, Yang, Zhu, Yuyan, Zhang, Weixiong
Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell
Externí odkaz:
http://arxiv.org/abs/2206.06145
Recently, Winter and Hahn [1] commented on our work on identifying subtypes of major psychiatry disorders (MPDs) based on neurobiological features using machine learning [2]. They questioned the generalizability of our methods and the statistical sig
Externí odkaz:
http://arxiv.org/abs/2206.04934
Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total
Externí odkaz:
http://arxiv.org/abs/2206.02970
Unveiling the underlying control principles of complex networks is one of the ultimate goals of network science. We introduce a novel concept, control hub, to reveal a cornerstone of the control structure of a network. The control hubs of a network a
Externí odkaz:
http://arxiv.org/abs/2206.01188
Autor:
Huo, Cuiying, He, Dongxiao, Li, Yawen, Jin, Di, Dang, Jianwu, Zhang, Weixiong, Pedrycz, Witold, Wu, Lingfei
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, whic
Externí odkaz:
http://arxiv.org/abs/2205.00256