Predicting Disease-related RNA Associations based on Graph Convolutional Attention Network

Zhang, Jinli; Hu, Xiaohua; Jiang, Zongli; Song, Bo; Quan, Wei; Chen, Zheng

doi:10.1109/bibm47256.2019.8983191

Cited by 15 publications

(6 citation statements)

References 24 publications

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“…Using the FastGCN algorithm and the Forest by Penalizing Attributes (Forest PA) classifier, Wang L. et al (2020) can accurately predict potential circRNA disease associations. In order to better learn the hidden representation of node features, Zhang J. et al (2019) used GCN combined with an attention mechanism to extract domain features and conducted experimental tests on two different RNA disease networks. There have also been some studies that used the autoencoder method on the graph to reconstruct node features.…”

Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

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Section: Typical Application Of Gnns In Bioinformaticsmentioning

confidence: 99%

Graph Neural Networks and Their Current Applications in Bioinformatics

et al. 2021

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“…The attention acts as an implicit regularizer by encouraging the model to focus on the most discriminative set of interactions between the nodes, often leading to better generalization on unseen data. Graph attention has been successful in biological applications, like predicting disease-RNA association [55] and essential gene prediction [42], as well as in non-biological applications, like machine translation [3] and image classification [35]. Finally, similar to max-pooling operations in standard convolutional networks, graph pooling allows us to aggregate information at each level of the hierarchy [9,31,52].…”

Section: Related Work: Deep Learning For Biological Data Analysismentioning

confidence: 99%

“…The attention focuses on the most discriminative set of interactions between the nodes, often leading to better generalization on unseen data. Graph attention has been successful in biological applications, like predicting disease-RNA association (Zhang et al, 2019) and essential gene prediction (Schapke et al, 2021). Finally, similar to the standard max-pooling operation, graph pooling allows us to aggregate information at each level of the hierarchy (Bruna et al, 2013;Lee et al, 2019;Ying et al, 2018).…”

Section: Related Work: Deep Learning For Biological Data Analysismentioning

confidence: 99%

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of Disease

Ghosal

Pergola

Chen

et al. 2021

Preprint

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We propose a novel deep neural network for whole-genome imaging-genetics. Our genetics module uses hierarchical graph convolution and pooling operations that mimic the organization of a well-established gene ontology to embed subject-level data into a latent space. The ontology implicitly tracks the convergence of genetic risk across biological pathways, and an attention mechanism automatically identifies the salient edges in our network. We couple the imaging and genetics data using an autoencoder and predictor, which couples the latent embeddings learned for each modality. The predictor uses these embeddings for disease diagnosis, while the decoder regularizes the model. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative biomarkers of each modality. We evaluate our framework on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and gene scores derived from Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross-validation, we show that our model achieves better classification performance than the baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Libo Huang . and biomedical research [5]. As GNN applications are getting more popular recently, high-performance systems are demanded to process large graph structures.…”

Section: Introductionmentioning

confidence: 99%

Analyzing GCN Aggregation on GPU

Kim

Jeong

et al. 2022

IEEE Access

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Graph convolutional neural networks (GCNs) are emerging neural networks for graph structures that include large features associated with each vertex. The operations of GCN can be divided into two phases -aggregation and combination. While the combination just performs matrix multiplications using trained weights and aggregated features, the aggregation phase requires graph traversal to collect features from adjacent vertices. Even though neural network applications rely on GPU's massively parallel processing, GCN aggregation kernels exhibit rather low performance since graph processing using compressed graph structures provokes frequent irregular accesses in GPUs. In order to investigate the performance hurdles of GCN aggregation on GPU, we perform an in-depth analysis of the aggregation kernels using real GPU hardware and a cycle-accurate GPU simulator. We first analyze the characteristics of the popular graph datasets used for GCN studies. We reveal the fractions of non-zero elements in feature vectors are diverse among datasets. Based on the observation, we build two types of aggregation kernels that handle uncompressed and compressed feature vectors. Our evaluation exhibits the performance of aggregation can be significantly influenced by kernel design approaches and feature density. We also analyze the individual loads that access the data arrays of the aggregation kernels to specify critical loads. Our analysis reveals the performance of GPU memory hierarchy is influenced by access patterns and feature size of graph datasets. Based on our observations we discuss possible kernel design approaches and architectural ideas that can improve the performance of GCN aggregation.INDEX TERMS GCN, aggregation kernel, GPU, characteristics.

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Predicting Disease-related RNA Associations based on Graph Convolutional Attention Network

Cited by 15 publications

References 24 publications

Graph Neural Networks and Their Current Applications in Bioinformatics

Graph Neural Networks and Their Current Applications in Bioinformatics

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of Disease

Analyzing GCN Aggregation on GPU

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