Results 31 to 40 of about 3,760 (232)
Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics [PDF]
EPJ Web of Conferences, 2021 Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive.
Hariri Ali +2 more
doaj +1 more source
StackVAE-G: An efficient and interpretable model for time series anomaly detection
AI Open, 2022 Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner.
Wenkai Li +4 more
doaj +1 more source
Constrained Graph Variational Autoencoders for Molecule Design
CoRR, 2018 8 pages, 5 ...
Qi Liu 0049 +3 more
openaire +3 more sources
Conditional Constrained Graph Variational Autoencoders for Molecule Design [PDF]
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the complex laws governing the chemical world. In this work, we explore the usage of the histogram of atom valences
Davide Rigoni +2 more
openaire +3 more sources
iScience, 2022 Summary: Large biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. We present a graph embedded topic model (GETM). We integrate existing biomedical knowledge graph information
Yuening Wang +4 more
doaj +1 more source
A Guassian Mixture Variational Graph Autoencoder for Node Classification [PDF]
AUT Journal of Modeling and SimulationGraph embedding is the procedure of transforming a graph into a low-dimensional, informative representation. The majority of existing graph embedding techniques have given less consideration to the embedding distribution of the latent codes and more ...
Mohadeseh Ghayekhlou, Ahmad Nikabadi
doaj +1 more source
iScience, 2023 Summary: Combinatorial drug therapy is a promising approach for treating complex diseases by combining drugs with synergistic effects. However, predicting effective drug combinations is challenging due to the complexity of biological systems and the ...
Wenyu Shan +3 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder
BMC Bioinformatics, 2023 Motivation Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex ...
Yuanyuan Zhang +4 more
doaj +1 more source
Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source