[HTML][HTML] Networks beyond pairwise interactions: Structure and dynamics

F Battiston, G Cencetti, I Iacopini, V Latora, M Lucas… - Physics reports, 2020‏ - Elsevier
The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Superhypergraph neural networks and plithogenic graph neural networks: Theoretical foundations

T Fujita - arxiv preprint arxiv:2412.01176, 2024‏ - arxiv.org
Hypergraphs extend traditional graphs by allowing edges to connect multiple nodes, while
superhypergraphs further generalize this concept to represent even more complex …

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

L Wang, Y Tan, X Yang, L Kuang… - Briefings in …, 2022‏ - academic.oup.com
In recent years, with the rapid development of techniques in bioinformatics and life science,
a considerable quantity of biomedical data has been accumulated, based on which …

Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction

Y Ma, Q Liu - Computers in Biology and Medicine, 2022‏ - Elsevier
The complex and diverse microbial communities are closely related to human health, and
the research of microbial communities plays an increasingly critical role in drug …

Microbes and complex diseases: from experimental results to computational models

Y Zhao, CC Wang, X Chen - Briefings in Bioinformatics, 2021‏ - academic.oup.com
Studies have shown that the number of microbes in humans is almost 10 times that of cells.
These microbes have been proven to play an important role in a variety of physiological …

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

L Peng, L Huang, G Tian, Y Wu, G Li, J Cao… - Frontiers in …, 2023‏ - frontiersin.org
Background Microbes have dense linkages with human diseases. Balanced
microorganisms protect human body against physiological disorders while unbalanced ones …

Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions

D Ruan, S Ji, C Yan, J Zhu, X Zhao, Y Yang, Y Gao… - Patterns, 2021‏ - cell.com
The continuous emergence of drug-target interaction data provides an opportunity to
construct a biological network for systematically discovering unknown interactions. However …

A survey on predicting microbe-disease associations: biological data and computational methods

Z Wen, C Yan, G Duan, S Li, FX Wu… - Briefings in …, 2021‏ - academic.oup.com
Various microbes have proved to be closely related to the pathogenesis of human diseases.
While many computational methods for predicting human microbe-disease associations …

GMMAD: a comprehensive database of human gut microbial metabolite associations with diseases

CY Wang, X Kuang, QQ Wang, GQ Zhang, ZS Cheng… - BMC genomics, 2023‏ - Springer
Background The natural products, metabolites, of gut microbes are crucial effect factors on
diseases. Comprehensive identification and annotation of relationships among disease …

Predicting microbe‐disease association based on heterogeneous network and global graph feature learning

Y Wang, X Lei, Y Pan - Chinese Journal of Electronics, 2022‏ - Wiley Online Library
Numerous microbes inhabit human body, making a vast difference in human health. Hence,
discovering associations between microbes and diseases is beneficial to disease …