Results 71 to 80 of about 2,491 (218)

MHCLSyn: Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction

Big Data Mining and Analytics
In the field of cancer treatment, drug combination therapy appears to be a promising treatment strategy compared to monotherapy. Recently, plenty of computational models are gradually applied to prioritize synergistic drug combinations.
Lei Li, Guodong Lü, Chunhou Zheng, Renyong Lin, Yansen Su   +4 more
doaj   +1 more source

Hypergraphs with arbitrarily small codegree Turán density

Bulletin of the London Mathematical Society, Volume 58, Issue 4, April 2026.
Abstract The codegree Turán density γ(F)$\gamma (F)$ of a k$k$‐graph F$F$ is the smallest γ∈[0,1)$\gamma \in [0,1)$ such that every k$k$‐graph H$H$ with δk−1(H)⩾(γ+o(1))|V(H)|$\delta _{k-1}(H)\geqslant (\gamma +o(1))\vert V(H)\vert$ contains a copy of F$F$. In this work, we show that for every ε>0$\varepsilon >0$, there is a k$k$‐uniform hypergraph F$F$
Simón Piga, Bjarne Schülke
wiley   +1 more source

Link-aware semi-supervised hypergraph [PDF]

, 2019
(#br)Hypergraph learning has been widely applied to various learning tasks. To ensure learning accuracy, it is essential to construct an informative hypergraph structure that effectively modulates data correlations.
Feiran Jie, Rongrong Ji, Liujuan Cao, Taisong Jin   +3 more
core   +1 more source

A Hypergraph Filtering Based Iterative Decoding Approach for Linear Channel Codes

IEEE Access, 2021
Efficient decoding for general linear channel codes has been a long standing problem. Message passing type algorithms can achieve near-optimal performance for channel codes with sparse check matrices and long code-lengths, but they may suffer from ...
Jiaqi He, Ruyuan Qu, Chongbin Xu, Hui Feng, Xin Wang   +4 more
doaj   +1 more source

Music Recommendation via Hypergraph Embedding

, 2023
In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to ...
Moscato V., Sperli G., La Gatta V., Pennone M., Postiglione M.   +4 more
core   +1 more source

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

Hypergraph neural networks: from signal processing to convolution, u-nets and beyond

Arce, Gonzalo R.Qian, WeiNetwork data has gained significant attention in signal processing and machine learning communities. Existing research mainly centers on simple graphs, which depict only pairwise connections.
Wang, Fuli
core   +1 more source

Learning with Hypergraphs: Clustering, Classification, and Embedding [PDF]

, 2007
We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected ...
Zhou, D., Huang, J., Schölkopf, B.
openaire   +3 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