Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction.

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node classification and graph classification. In the existing GNAS frameworks, the designed graph neural network (GNN) architectures learn the representation of homogenous graphs with one type of relationship connecting two nodes. However, multi-view graphs, where each view represents a type of relationship among nodes, are ubiquitous in the real world. The traditional GNAS frameworks learn the graph representation without considering the interactions between nodes and multiple relationships, so they fail to solve multi-view graph-based problems, such as multi-view graphs modelling the biomedical entity and relation extraction tasks. In this paper, we propose MVGNAS, a multi-view graph neural network automatic modelling framework for biomedical entity and relation extraction, to resolve this challenge. In MVGNAS, we propose an automatic multi-view representation learning to learn low-dimensional representations of nodes that capture multiple relationships in a multi-view graph, representing the first research work in literature to solve the problem of multi-view graph representation learning architecture search for biomedical entity and relation extraction tasks. The experimental results demonstrate that MVGNAS can achieve the best performance in biomedical entity and relation extraction tasks against the state-of-the-art baseline methods.

AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction.

It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure's features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics.

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Light simplified cultivation and high quality rice are the main directions of rice production in China. Meteorological factors are the most important environmental factors affecting rice growth and yield. Few studies examined the relationship between rice yield and microclimate under different light simplified cultivation modes. To explore the relationship between rice yield and climatic factors (temperature, sunshine and water) at different growth stages of hybrid rice under different forecrops in southwest China, we carried out a split-plot design experiment in 2019 and 2020, with two forecrops of green cabbage and rape as the main plot, and three planting methods, direct-seeding, blanket-transplanting, and artificial transplanting as the subplots, taking Yixiangyou 2115 as the experimental variety. Results showed that compared with rape-paddy cropping system, cabbage-paddy cropping system significantly improved the accumulated temperature and precipitation production efficiency and consequently improved the effective panicles, setting rate, and 1000-grain weight. The yield was increased by 12.7% and 8.3% under cabbage-paddy and rape-paddy cropping system, respectively. Compared with manual transplanting, mechanical transplanting improved effective panicles, production efficiency of radiation, accumulated temperature and precipitation, and the radiation use efficiency of grain during the whole growth period. The mean yield was increased by 4.6% in 2019 and 2020. However, the above parameters of direct-seeding significantly decreased, but the yield decreased by 8.7%. Compared with 2019, mechanical transplanting and artificial transplanting were sown one month earlier in 2020 under the same stubble, which shortened growth period, reduced air temperature, and increased precipitation after flowering, leading to a significant decrease in effective accumulated temperature and light radiation; production efficiency of accumulated temperature, light energy, and precipitation; and utilization efficiency of light energy of grain, spikelets per panicle, setting rate, and 1000-grain weight. However, the yield was significantly reduced. Partial least squares regression analysis was used to establish the production forecast equation of standardized regression coefficients of meteorological factors. There was a positive correlation between rice yield and effective accumulated temperature and total radiation during the growth stage or the whole growth period. In addition, there was a significant negative correlation between rice yield and precipitation during the whole growth period. In conclusion, mechanical transplanting under cabbage-paddy cropping system was a rice planting method that optimised the seasonal sunshine and temperature resources in southwest China. The method facilitated the full utilization of temperature and sunshine resources, resulting in high yield. However, it was not advisable to sow or transplant too early.

Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network.

In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this work, we design a novel Multimodal Graph Neural Network (MGNN)framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.