15 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.…
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…
Luiz Anastacio Alves, Natiele Carla da Silva Ferreira, Victor Maricato, Anael Viana Pinto Alberto + 2 more
'Victor Maricato' 'Anael Viana Pinto Alberto' 'Evellyn Araujo Dias' 'Nt Jose Aguiar Coelho'] Despite the increasing number of pharmaceutical companies, university laboratories and funding, less than one percent of initially researched drugs enter the commercial market. In this context, virtual screening (VS) has gained…
Shun Li, Yuxuan Tao, Enhao Tang, Ting Xie + 2 more
'Miriam Leeser'] Graph convolutional networks (GCNs) based on convolutional operations have been developed recently to extract high-level representations from graph data. They have shown advantages in many critical applications, such as recommendation system, natural language processing, and prediction of chemical…
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…
Daniel Walke, Daniel Micheel, Kay Schallert, Thilo Muth + 3 more
'David Broneske' 'Gunter Saake' 'Robert Heyer'] Title: Abstract The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving…
Qingju Jiao, Peige Zhao, Hanjin Zhang, Yahong Han + 2 more
'Toqir Rana'] Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the…
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…
Fangyuan Lei, Xun Liu, Qingyun Dai, Bingo Wing-Kuen Ling + 2 more
'Huimin Zhao' 'Yan Liu'] With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity.…
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
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…
Xun Zhang, Lanyan Yang, Bin Zhang, Ying Liu + 4 more
'Xiaohai Qin' 'Mengmeng Hao' 'Jaesung Lee'] The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to…
Zhonglin Ye, Zhuoran Li, Gege Li, Haixing Zhao
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks. However, current dual-channel graph convolutional neural networks are…