Results 21 to 30 of about 3,760 (232)
Drug repositioning based on heterogeneous networks and variational graph autoencoders
Frontiers in Pharmacology, 2022 Predicting new therapeutic effects (drug repositioning) of existing drugs plays an important role in drug development. However, traditional wet experimental prediction methods are usually time-consuming and costly.
Song Lei, Xiujuan Lei, Lian Liu
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Network Embedding Algorithm Taking in Variational Graph AutoEncoder
Mathematics, 2022 Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common ...
Dongming Chen +4 more
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Dataset Recommendation via Variational Graph Autoencoder [PDF]
2019 IEEE International Conference on Data Mining (ICDM), 2019 This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user’s research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user’s research need.
Basmah Altaf +3 more
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Continuous Representation of Molecules Using Graph Variational Autoencoder [PDF]
CoRR, 2020 In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is employed to prune the latent space and help the decoder in generating meaningful adjacency tensor of molecules. Other than the potential applicability in drug design and property
Mohammadamin Tavakoli, Pierre Baldi
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Open Knowledge Graphs Canonicalization using Variational Autoencoders [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Accepted to EMNLP ...
Sarthak Dash +4 more
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Multiview Variational Graph Autoencoders for Canonical Correlation Analysis [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 4 pages, 3 figures ...
Yacouba Kaloga +4 more
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Machine Learning: Science and Technology, 2023 Cell type identification using single-cell RNA sequencing data is critical for understanding disease mechanisms and drug discovery. Cell clustering analysis has been widely studied in health research for rare tumor cell detection.
Eric Lin +5 more
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Variational graph autoencoders for multiview canonical correlation analysis [PDF]
Signal Processing, 2021 Abstract We present a novel approach for multiview canonical correlation analysis based on a variational graph neural network model. We propose a nonlinear model which takes into account the available graph-based geometric constraints while being scalable to large-scale datasets with multiple views.
Kaloga, Yacouba +4 more
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SGVAE: Sequential Graph Variational Autoencoder
CoRR, 2019 Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process,
Bowen Jing, Ethan A. Chi, Jillian Tang
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Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning
Applied Sciences, 2022 Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities.
Hongmei Tang +5 more
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