12 papers · ranked by Valyu relevance
Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of…
Hoa Xuan Nguyen, Shaoshu Zhu, Mingming Liu, Uttam Ghosh + 3 more
'Sahil Verma' 'Gautam Srivastava' 'Mouhammd Alkasassbeh'] Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice…
Huu Ngoc Tran Tran, J. Joshua Thomas, Nurul Hashimah Ahamed Hassain Malim, Yuriy Orlov
'Nurul Hashimah Ahamed Hassain Malim' 'Yuriy Orlov'] The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage.…
Riccardo Smeriglio, Joana Rosell-Mirmi, Petia Radeva, Jordi Abante
Current genotype-to-phenotype models, such as poly-genic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the…
Shujuan Cao, Xiaoming Wang, Zhonglin Ye, Mingyuan Li + 1 more
The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network…
Bastian Pfeifer, Afan Secic, Anna Saranti, Andreas Holzinger
The tremendous success of graphical neural networks (GNNs) has already had a major impact on systems biology research. For example, GNNs are currently used for drug target recognition in protein-drug interaction networks as well as cancer gene discovery and more. Important aspects whose practical relevance is often…
Xiaoxiao Li, Yuan Zhou, Siyuan Gao, Nicha Dvornek + 7 more
Understanding how certain brain regions relate to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. In…
Zhao Chen, Danilo Comminiello, Tokunbo Ogunfunmi, Nithin V. George
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node…
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…
Giulia Muzio, Leslie O’Bray, Karsten Borgwardt
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in…
Ragini Kihlman, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann + 1 more
'D-J de Koning'] Title: Abstract In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, genomic markers can now be allocated…
David Buterez, Ioana Bica, Ifrah Tariq, Helena Andrés-Terré + 1 more
Currently, single-cell RNA sequencing (scRNA-seq) allows high-resolution views of individual cells, for libraries of up to (tens of) thousands of samples. In this study, we introduce the use of graph neural networks (GNN) in the unsupervised study of scRNA-seq data, namely for dimensionality reduction and clustering.…