MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement

Gao, Jianliang; Zhang, Xiangchi; Tian, Ling; Liu, Yuxin; Wang, Jianxin; Zhao, Li; Hu, Xiaohua

doi:10.1016/j.ymeth.2021.10.005

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…Therefore, few-shot learning has become an effective method for information extraction. Gao et al [ 9 ] proposed a multitask graph neural network based on few-shot learning for disease similarity measurement. Lu et al [ 10 ] built a few-shot learning-based classifier by limiting training samples for food recognition.…”

Section: Introductionmentioning

confidence: 99%

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

Zhang

Dong

et al. 2022

Computational Intelligence and Neuroscience

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Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

Zhang

Dong

et al. 2022

Computational Intelligence and Neuroscience

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“…This issue has also been studied by Gao et al [32]. Also in this case, the similarity was computed via node embeddings; however, the authors implemented a link prediction task to overcome the insufficient number of labeled similar disease pairs.…”

Section: Related Workmentioning

confidence: 99%

Identifying Candidate Gene–Disease Associations via Graph Neural Networks

Cinaglia

Cannataro

2023

Entropy

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Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene–disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford’s BioSNAP was also processed for performance evaluation only.

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“…Thus, the research of deep time series forecasting models becomes prevalent. Starting from RNN [3], [4], [5], [24], [57], [68], [69], popular networks which are successful in other research fields are successively applied to time series forecasting, like CNN [1], [2], [18], [22], [52], GNN [9], [10], [70] and Transformer [6], [7], [31], [36], [37], [41], [71]. They are mainly built upon the hypothesis that time series are causal, auto-regressive and stationary.…”

Section: Appendix B Related Workmentioning

confidence: 99%

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

Shen¹,

Wei²,

Wang³

2022

Preprint

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How to handle time features shall be the core question of any time series forecasting model. Ironically, it is often ignored or misunderstood by deep-learning based models, even those baselines which are state-of-the-art. This behavior makes their inefficient, untenable and unstable. In this paper, we rigorously analyze three prevalent but deficient/unfounded deep time series forecasting mechanisms or methods from the view of time series properties, including normalization methods, multivariate forecasting and input sequence length. Corresponding corollaries and solutions are given on both empirical and theoretical basis. We thereby propose a novel time series forecasting network, i.e. RTNet, on the basis of aforementioned analysis. It is general enough to be combined with both supervised and self-supervised forecasting format. Thanks to the core idea of respecting time series properties, no matter in which forecasting format, RTNet shows obviously superior forecasting performances compared with dozens of other SOTA time series forecasting baselines in three real-world benchmark datasets. By and large, it even occupies less time complexity and memory usage while acquiring better forecasting accuracy. The source code is available at https://github.com/OrigamiSL/RTNet.

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MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement

Cited by 13 publications

References 23 publications

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

Identifying Candidate Gene–Disease Associations via Graph Neural Networks

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

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