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    Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer by Dawei Jiang, Zixi Chen, Zixi Chen, Hongli Du

    Published 2025-03-01
    “…Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. …”
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    Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data by Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu, Sibo Cheng

    Published 2025-06-01
    “…However, the selection of deep learning methods for wildfire prediction remains uncertain due to the lack of comparative analysis in a quantitative and explainable manner, crucial for improving prevention measures and refining models. …”
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    DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations by Yuyao Yan, Xinyi Chai, Jiajun Liu, Sijia Wang, Wenran Li, Tao Huang

    Published 2025-04-01
    “…Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. …”
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    StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models. by Amanda Bleichrodt, Amelia Phan, Ruiyan Luo, Alexander Kirpich, Gerardo Chowell

    Published 2025-01-01
    “…<h4>Methods</h4>StatModPredict supports robust real-time forecasting and retrospective model analysis, including fitting, forecasting, evaluation, visualization, and comparison of results from four popular models. …”
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    Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery by Yingcan Li, Yu Shen, Yezi Cai, Yulin zhang, Jiahui Gao, Lei Huang, Weinuo Si, Kai Zhou, Shan Gao, Qichao Luo

    Published 2025-03-01
    “…Abstract Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. …”
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