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deep graph infomax (dgi) model (InfoMax Inc)

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InfoMax Inc deep graph infomax (dgi) model
Deep Graph Infomax (Dgi) Model, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/graph+model/deep+graph+infomax/pmc11696235-17-9-7
Average 90 stars, based on 1 article reviews

deep graph infomax (dgi) model - by Bioz Stars, 2026-10

90/100 stars

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Article Title: Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling
Article Snippet: In this paper, we propose a large-scale heterogeneous network embedding learning model LHGI (Large-scale Heterogeneous Graph Infomax) based on metapath graph sampling technology.

Article Title: Object-Level Visual-Text Correlation Graph Hashing for Unsupervised Cross-Modal Retrieval
Article Snippet: Then, the Deep Graph Infomax model is trained for 30 epochs to map the image object region into a 512-dimensional graph-level representation.

Article Title: Application of Computational Biology and Artificial Intelligence in Drug Design
Article Snippet: The pretrained graph neural networks model 3D Infomax is an example, it can predict latent 3D and quantum in-formation by utilizing the 2D molecular graphical data, which is beneficial the down-stream tasks of molecule generation and molecular property prediction [ ].

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Article Title: Comparative analysis of molecular fingerprints in prediction of drug combination effects
Article Snippet: .. Infomax fingerprints are 300 bits long, generated using a pre-trained Deep Graph Infomax model that by design maximizes mutual information between local and global molecular graph features [ , ]. ..

Article Title: Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism.
Article Snippet: Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific.. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently.. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs.

Article Title: Unsupervised Co-Optimization of A Graph Neural Network and A Knowledge Graph Embedding Model to Prioritize Causal Genes for Alzheimer’s Disease
Article Snippet: .. The mean rank (MR) and Hits@K metrics can be computed using equations 6 and 7 for the rankings yielded by a knowledge graph embedding model to the genes in the silver standard geneset. (6) (7) Calculation of rank based evaluation metrics for the gene rankings generated by co-optimized embeddings from deep graph infomax model The strategy that we have adopted for evaluating the gene rankings generated by the co-optimized embeddings from the deep graph infomax model is observing the mean rank of the silver standard genes (ranked purely using the gene scores from deep graph infomax algorithm) in the context of the fully ranked geneset. ..

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