Results 61 to 70 of about 1,216 (173)

Large language models for bioinformatics

Quantitative Biology, Volume 14, Issue 1, March 2026.
Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
Wei Ruan, Yanjun Lyu, Jing Zhang, Jiazhang Cai, Peng Shu, Yang Ge, Yao Lu, Shang Gao, Yue Wang, Peilong Wang, Lin Zhao, Tao Wang, Yufang Liu, Luyang Fang, Ziyu Liu, Zhengliang Liu, Yiwei Li, Zihao Wu, Junhao Chen, Hanqi Jiang, Yi Pan, Zhenyuan Yang, Jingyuan Chen, Shizhe Liang, Wei Zhang, Terry Ma, Yuan Dou, Jianli Zhang, Xinyu Gong, Qi Gan, Yusong Zou, Zebang Chen, Yuanxin Qian, Shuo Yu, Jin Lu, Kenan Song, Xianqiao Wang, Andrea Sikora, Gang Li, Xiang Li, Quanzheng Li, Yingfeng Wang, Lu Zhang, Yohannes Abate, Lifang He, Wenxuan Zhong, Rongjie Liu, Chao Huang, Wei Liu, Ye Shen, Ping Ma, Hongtu Zhu, Yajun Yan, Dajiang Zhu, Tianming Liu   +54 more
wiley   +1 more source

Attention-Based Hypergraph Neural Network: A Personalized Recommendation

Applied Sciences
Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience.
Peihua Xu, Maoyuan Zhang
doaj   +1 more source

A Review of Hypergraph Neural Networks

EAI Endorsed Transactions on e-Learning
In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships.
openaire   +1 more source

Tensorized Hypergraph Neural Networks

Hypergraph neural networks (HGNN) have recently become attractive and received significant attention due to their excellent performance in various domains. However, most existing HGNNs rely on first-order approximations of hypergraph connectivity patterns, which ignores important high-order information.
Maolin Wang 0001, Yaoming Zhen, Yu Pan 0005, Yao Zhao 0001, Chenyi Zhuang, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao 0001   +7 more
openaire   +2 more sources

Heterogeneous Temporal Hypergraph Neural Network

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex networks, GRL methods designed for complex heterogeneous temporal graphs (HTGs) have been proposed and have achieved successful applications in various fields.
Huan Liu 0001, Pengfei Jiao, Mengzhou Gao 0001, Chaochao Chen 0001, Di Jin 0001   +4 more
openaire   +2 more sources

A comprehensive review of cluster methods for drug–drug interaction network

Quantitative Biology, Volume 14, Issue 1, March 2026.
Abstract The detection of drug–drug interaction (DDI) is crucial to the rational use of drug combinations. Experimentally, DDI detection is time‐consuming and laborious. Currently, researchers have developed a variety of computational methods to predict DDI.
Shuyuan Cao, Guixia Liu, Xiangrun Zhou, Ji Lv   +3 more
wiley   +1 more source

Cognitive Networks for Knowledge Modeling: A Gentle Introduction for Data‐ and Cognitive Scientists

WIREs Cognitive Science, Volume 17, Issue 2, March/April 2026.
Cognitive network science helps organize associative knowledge—that is, the connections between concepts. These connections play a key role in cognitive processes such as language understanding and context interpretation, even though they are not obvious in language use.
Edith Haim, Massimo Stella
wiley   +1 more source

Generalization Performance of Hypergraph Neural Networks

Proceedings of the ACM on Web Conference 2025
Hypergraph neural networks have been promising tools for handling learning tasks involving higher-order data, with notable applications in web graphs, such as modeling multi-way hyperlink structures and complex user interactions. Yet, their generalization abilities in theory are less clear to us.
Yifan Wang, Gonzalo R. Arce, Guangmo Tong   +2 more
openaire   +2 more sources

Lightweight Hybrid Wafer Defect Pattern Network Based on Feedforward Efficient Attention

CAAI Transactions on Intelligence Technology, Volume 11, Issue 1, Page 149-166, February 2026.
ABSTRACT With the increase of semiconductor integration density, in order to cope with the increase of wafer defect complexity and types, especially the low recognition accuracy of overlapping mixed defects and unknown wafer defects, this study proposes a lightweight model for wafer defect detection called LightWMNet.
Zhiqiang Hu, Yiquan Wu
wiley   +1 more source

Stability Analysis of Different Quaternion‐Valued Impulsive BAM Neural Networks With Unknown Parameters and Time‐Varying Delays

Engineering Reports, Volume 8, Issue 1, January 2026.
This work tackles the unresolved stability problem of heterogeneous quaternion‐valued BAM neural networks plagued by unknown parameters, time‐varying delays, and impulses. By synergizing Lyapunov theory with inequality techniques, we establish rigorous, yet practical, global stability conditions.
Xi Long, Yaqin Li
wiley   +1 more source