Zobrazeno 1 - 10
of 721
pro vyhledávání: '"AI Xing"'
Publikováno v:
罕见病研究, Vol 3, Iss 2, Pp 168-174 (2024)
The unmet clinical needs of patients with rare diseases persist. Many rare diseases lack effective treatments, and drug development for rare diseases faces greater challenges than that for common multiple diseases. In recent years, the concept of " p
Externí odkaz:
https://doaj.org/article/0033f134d06948d9b705cc3c25046aff
Publikováno v:
Meikuang Anquan, Vol 53, Iss 5, Pp 230-235 (2022)
Coal Mine Fire Prevention and Control Rules is the first comprehensive normative document on coal mine fire prevention and control in China, which requires that coal mine fire prevention and control work should follow the principle of “prevention f
Externí odkaz:
https://doaj.org/article/6b159ac25ac94b0bbb1e9fd25684af12
Autor:
TANG Ling, ZHANG Jie, ZHAO Boyuan, AI Xing, WANG Chaoyun, SE RI Geleng, LI Yuanhong, YANG Zhimin
Publikováno v:
罕见病研究, Vol 1, Iss 1, Pp 78-83 (2022)
The incidence of each of the rare disease is very low. The complexity and diagnosis difficulty of the rare disease lead to the difficulties in the clinical research and development (R&D) of drugs for rare diseases. There is an urgent clinical need fo
Externí odkaz:
https://doaj.org/article/68e11d504d7d4c4daec9d323a0d46aa2
Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding generation relies on graph topology. Existing efforts are dedicated to pur
Externí odkaz:
http://arxiv.org/abs/2408.16537
Signed graphs consist of edges and signs, which can be separated into structural information and balance-related information, respectively. Existing signed graph neural networks (SGNNs) typically rely on balance-related information to generate embedd
Externí odkaz:
http://arxiv.org/abs/2401.10590
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishin
Externí odkaz:
http://arxiv.org/abs/2308.01063
We investigate adversarial robustness of unsupervised Graph Contrastive Learning (GCL) against structural attacks. First, we provide a comprehensive empirical and theoretical analysis of existing attacks, revealing how and why they downgrade the perf
Externí odkaz:
http://arxiv.org/abs/2307.12555
In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial data analysis. However, existing methods, especially entropy based graph
Externí odkaz:
http://arxiv.org/abs/2303.13543
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning. Compared with nonlinear Graph Neural Network (GNN) models, linearized GNNs are much more time-efficient and can achieve comparab
Externí odkaz:
http://arxiv.org/abs/2302.00371