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Computer Graphics

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DGCNN: A convolutional neural network over large-scale labeled graphs.

Neural networks : the official journal of the International Neural Network Society
Exploiting graph-structured data has many real applications in domains including natural language semantics, programming language processing, and malware analysis. A variety of methods has been developed to deal with such data. However, learning grap...

Using predicate and provenance information from a knowledge graph for drug efficacy screening.

Journal of biomedical semantics
BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relation...

The Vapnik-Chervonenkis dimension of graph and recursive neural networks.

Neural networks : the official journal of the International Neural Network Society
The Vapnik-Chervonenkis dimension (VC-dim) characterizes the sample learning complexity of a classification model and it is often used as an indicator for the generalization capability of a learning method. The VC-dim has been studied on common feed-...

Deep Neural Representation Guided Face Sketch Synthesis.

IEEE transactions on visualization and computer graphics
Face sketch synthesis shows great applications in a lot of fields such as online entertainment and suspects identification. Existing face sketch synthesis methods learn the patch-wise sketch style from the training dataset containing photo-sketch pai...

Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

Journal of chemical information and modeling
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically ...

Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks.

Cell systems
Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot ...

Multi-target drug repositioning by bipartite block-wise sparse multi-task learning.

BMC systems biology
BACKGROUND: Finding potential drug targets is a crucial step in drug discovery and development. Recently, resources such as the Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 database provide gene expression profiles induced by...

Human Splice-Site Prediction with Deep Neural Networks.

Journal of computational biology : a journal of computational molecular cell biology
Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNN...

Practical Time-Varying Formation Tracking for Second-Order Nonlinear Multiagent Systems With Multiple Leaders Using Adaptive Neural Networks.

IEEE transactions on neural networks and learning systems
Practical time-varying formation tracking problems for second-order nonlinear multiagent systems with multiple leaders are investigated using adaptive neural networks (NNs), where the time-varying formation tracking error caused by time-varying exter...