Results 161 to 170 of about 2,491 (218)

HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning. [PDF]

Bioinformatics
Liu X, Zhang N, Kang C, Ai C, Yang H, Tang J, Guo F.   +6 more
europepmc   +1 more source

MFH-LPI: based on multi-view similarity networks fusion and hypergraph learning for long non-coding RNA-protein interactions prediction. [PDF]

BMC Genomics
Xing Z, Yu S, Liao S, Wang P, Liao B.
europepmc   +1 more source

pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning. [PDF]

Brief Bioinform
Ma J, Wang Z, Tong C, Yang Q, Zhang L, Liu H.   +5 more
europepmc   +1 more source

HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA-Disease Association Prediction. [PDF]

Bioengineering (Basel)
Lu D, Li J, Zheng C, Liu J, Zhang Q.
europepmc   +1 more source

Hybrid Directed Hypergraph Learning and Forecasting of Skeleton-Based Human Poses. [PDF]

Cyborg Bionic Syst
Cui Q, Ding Z, Chen F.
europepmc   +1 more source

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes. [PDF]

Brief Bioinform
Xia S, Li D, Deng X, Liu Z, Zhu H, Liu Y, Li D.   +6 more
europepmc   +1 more source

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning. [PDF]

Brain Imaging Behav, 2019
Zu C, Gao Y, Munsell B, Kim M, Peng Z, Cohen JR, Zhang D, Wu G.   +7 more
europepmc   +1 more source

Multi-Hypergraph Learning for Incomplete Multimodality Data. [PDF]

IEEE J Biomed Health Inform, 2018
Liu M, Liu M, Gao Y, Yap PT, Shen D.
europepmc   +1 more source

Learning on Hypergraphs with Sparsity

Learning on Hypergraphs with Sparsity
Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed.
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