LAUSR

MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality.

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1 Published in 2022 at "IEEE/ACM transactions on computational biology and bioinformatics"

DOI: 10.1109/tcbb.2022.3221736

Abstract: Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional… read more here.

Keywords: synthetic lethality; multiple attention; attention graph; lethality ... See more keywords

Hyperspectral Image Classification Based on Deep Attention Graph Convolutional Network

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2 Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2021.3066485

Abstract: Hyperspectral images (HSIs) have gained high spectral resolution due to recent advances in spectral imaging technologies. This incurs problems, such as an increased data scale and an increased number of bands for HSIs, which results… read more here.

Keywords: attention; graph convolutional; based deep; graph ... See more keywords

Dual Attention Graph Convolutional Network for Relation Extraction

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0 Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2023.3289879

Abstract: Dependency-based models are widely used to extract semantic relations in text. Most existing dependency-based models establish stacked structures to merge contextual and dependency information, which encode the contextual information first and then encode the dependency… read more here.

Keywords: dependency; information; graph convolutional; dual attention ... See more keywords

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

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1 Published in 2022 at "Computational Intelligence and Neuroscience"

DOI: 10.1155/2022/6241373

Abstract: The extractive summarization approach involves selecting the source document's salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of… read more here.

Keywords: graph; attention graph; extractive summarization; model ... See more keywords

Pedestrian Trajectory Prediction in Crowded Environments Using Social Attention Graph Neural Networks

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0 Published in 2024 at "Applied Sciences"

DOI: 10.3390/app14209349

Abstract: Trajectory prediction is a key component in the development of applications such as mixed urban traffic management and public safety. Traditional models have struggled with the complexity of modeling dynamic crowd interactions, the intricacies of… read more here.

Keywords: social attention; attention; graph neural; attention graph ... See more keywords