Results 61 to 70 of about 1,157 (178)

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

Multiview Hypergraph Fusion Network for Change Detection in High-Resolution Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Currently, convolutional neural networks and transformers have been the dominant paradigms for change detection (CD) thanks to their powerful local and global feature extraction capabilities. However, with the improvement of resolution, spatial, spectral,
Xue Zhao, Kai Zhang, Feng Zhang, Jiande Sun, Wenbo Wan, Huaxiang Zhang   +5 more
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

Fault diagnosis of shearer cutting unit gearbox based on improved cascaded broad learning

Gong-kuang zidonghua
The vibration monitoring data of the shearer cutting unit gearbox has a complex structure and is prone to class imbalance issues, leading to frequent false positives in traditional machine learning-based fault diagnosis methods.
LI Xin, LI Shuhua, CHEN Hao, SI Lei, WEI Dong, ZOU Xiaoyu   +5 more
doaj   +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

Bi-View Contrastive Learning with Hypergraph for Enhanced Session-Based Recommendation

Information
Session-based recommendation (SBR) aims to predict a user’s next interests based on their actions in a single visit. Recent methods utilize graph neural networks to study the pairwise relationship of item transfers, yet these often overlook the complex ...
Zijun Wang, Lai Wei
doaj   +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