MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality.
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization
Sign Up to like & getrecommendations! 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