GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM

Liang, Yigao; Jiang, Shaohua; Min, Gao; Jia, Fengjiao; Wu, Zaoyang; Lyu, Zhijian

doi:10.1088/1742-6596/2219/1/012008

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…This model extended the time-dependent extension of the graph learning model. Hybrid models based on graph learning have been applied in many fields, such as biology [41], pharmacy [42], network interconnection [43], etc.…”

Section: Related Workmentioning

confidence: 99%

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Xu,

Seng,

Ang

2023

Electronics

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Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides’ positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go.

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Section: Related Workmentioning

confidence: 99%

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Xu,

Seng,

Ang

2023

Electronics

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“…Compared with the traditional LSTM method, Bi-LSTM improves prediction accuracy. In addition, LSTM also has a good performance on time-based case analysis, they proposed the GLSTM-DTA for DTA prediction, which combined graph neural network (GNN) with LSTM to extract drug features and protein features, which is facilitating to capture of long-term dependencies in sequence [24]. In addition, the Graph LSTM addresses the limitations of sequential models, and also help to utilize the semantic correlation between cases and categories on the temporal [25].…”

Section: Time-series Geographic Information Analysismentioning

confidence: 99%

An early warning model of type 2 diabetes risk based on POI visit history and food access management

Xie¹,

Li²,

Wang³

et al. 2023

PLoS ONE

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Type 2 diabetes (T2D) is a long-term, highly prevalent disease that provides extensive data support in spatial-temporal user case data mining studies. In this paper, we present a novel T2D food access early risk warning model that aims to emphasize health management awareness among susceptible populations. This model incorporates the representation of T2D-related food categories with graph convolutional networks (GCN), enabling the diet risk visualization from the geotagged Twitter visit records on a map. A long short-term memory (LSTM) module is used to enhance the performance of the case temporal feature extraction and location approximate predictive approach. Through an analysis of the resulting data set, we highlight the food effect category has on T2D early risk visualization and user food access management on the map. Moreover, our proposed method can provide suggestions to T2D susceptible patients on diet management.

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“…Theoretically, when GNNs are combined with gated recurrent units (GRUs), the GNN captures the feature relationships between nodes 5 , while the GRU captures the temporal dependencies of these features. Recent studies have reported using GNN-GRU models for spatiotemporal traffic flow prediction 6 .…”

Section: Introductionmentioning

confidence: 99%

Leveraging Graph Neural Networks and Gate Recurrent Units for Accurate and Transparent Prediction of Baseball Pitching Speed

Yang,

Jin,

Chen

2024

Preprint

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Long Short-Term Memory (LSTM) networks are widely used in biomechanical data analysis but have significant limitations in interpretability and decision transparency. Combining Graph Neural Networks (GNNs) with Gate Recurrent Units (GRUs) may offer a better solution. This study proposes and validates a hybrid GNN-GRU model for predicting baseball pitching speed, enhancing its interpretability using Layer-wise Relevance Propagation (LRP). C3D data from 53 baseball players were downloaded from a public dataset. Kinematic features of 9 joints and pitching speed during the pitching process were calculated using Visual3D, resulting in a total of 208 valid pitches. The feature data were input into both LSTM and GNN-GRU hybrid models, with hyperparameters tuned using Particle Swarm Optimization (PSO). LRP was employed to obtain the contribution rate changes of kinematic features to the prediction results throughout the pitching cycle. The prediction accuracy of the models was evaluated using Mean Absolute Error (MAE) and R-squared (R²). The results showed that there were significant statistical differences in the MAE and R2 metrics between the LSTM model and the GNN-GRU model in predicting pitching speed on the test set. The MAE (P = 0.000, Z = -5.170, Cohen's d =1.514) and R2(P = 0.000, Z = -2.981, Cohen's d =2.314) of the LSTM model were significantly lower than those of the GNN-GRU model. Compared to LSTM, the GNN-GRU model achieved better prediction accuracy but was more susceptible to the influence of data outliers. Moreover, the GNN-GRU-based model demonstrated better interpretability and decision transparency.

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GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM

Cited by 7 publications

References 25 publications

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

An early warning model of type 2 diabetes risk based on POI visit history and food access management

Leveraging Graph Neural Networks and Gate Recurrent Units for Accurate and Transparent Prediction of Baseball Pitching Speed

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