担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）   出版者・発行元：Frontiers Media SA  

Background

Influenza remains a significant public health challenge worldwide, necessitating robust forecasting models to facilitate timely interventions and resource allocation. The aim of this study was to develop a long short-term memory (LSTM)-based short-term forecasting model to accurately predict weekly influenza case counts in Tokyo, Japan.

Method

By using weekly time-series data on influenza incidence in Tokyo from 2000 to 2019, along with meteorological variables, we developed four distinct models to evaluate the impact of the external variables of mean temperature, relative humidity, and national public holidays. After model training, we assessed the predictive performance on an independent test dataset, using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient.

Results

During the study period, 1,445,944 influenza cases were analyzed. The model incorporating all three external variables demonstrated superior predictive accuracy, with an MSE of 3,646,084, RMSE of 1,909, MAE of 849, and Pearson’s correlation coefficient of 0.924. These findings underscore the substantial contribution of these external factors to improving the prediction performance.

Conclusion

This study highlighted the efficacy of LSTM-based models for short-term influenza forecasting and reinforces the importance of integrating meteorological variables and national public holidays into predictive frameworks. Our optimal model provided more precise forecasts of influenza activity in Tokyo, Japan.

DOI： 10.3389/fpubh.2025.1618508

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担当区分：筆頭著者   出版者・発行元：Springer Science and Business Media LLC  

Abstract <p>In recent years, deep neural networks (DNNs) have been applied for constructing molecular latent representations for drug discovery. The quality of these representations, obtained using DNN encoders, affects the generalization performance of the model, i.e., its ability to predict molecular properties for previously unseen compounds. Given the vast space of potential organic compounds and limited availability of labeled data with specific molecular properties, enhancing the generalization performance of predictive models is key for accelerating drug discovery. This requires the construction of effective molecular latent representations. Considering this aspect, this paper introduces the graph latent diffusion autoencoder (Graph LDA), a deep molecular generative model that combines a graph-transformer-based variational autoencoder and latent-diffusion-based latent prior model, designed for constructing generalized molecular representation through unsupervised learning. To assess the generalization performance of molecular property predictions based on the constructed molecular representations, the results for Graph LDA were compared with those of existing models using the widely applicable information criterion (WAIC) and widely applicable Bayesian information criterion (WBIC). The results indicated that Graph LDA outperformed the existing methods. Furthermore, we empirically demonstrated that the superior generalization performance of Graph LDA is attributable to the smoothness and multimodality of its learned molecular latent representation. The proposed robust framework for molecular property prediction holds significant potential for accelerating drug discovery and material advancements.Scientific contribution :This work introduces Graph LDA, a novel deep molecular generative model that combines a graph-transformer-based variational autoencoder and latent-diffusion-based latent prior model. The proposed model can extract smooth molecular latent representations with multimodal distributions, resulting in high generalization performance for molecular property prediction. Results of WAIC and WBIC analyses demonstrate that Graph LDA significantly outperforms existing representative unsupervised representation learning models.</p>

DOI： 10.21203/rs.3.rs-6299941/v1

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その他リンク： https://www.researchsquare.com/article/rs-6299941/v1.html

担当区分：筆頭著者  

添付ファイル： 2312.13110.pdf

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担当区分：筆頭著者  

添付ファイル： GraphLDA.pdf

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担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/minf.202000203

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その他リンク： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/minf.202000203

総ページ数：3, 6, 244, 6, 図版26p   記述言語：日本語

CiNii Books

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開催年月日： 2024年10月

ポスター発表

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