Xiumin Shi

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每年的科研成果

与联合国可持续发展目标相关的专业知识

2015 年，联合国成员国同意 17 项可持续发展目标 (SDG)，以消除贫困、保护地球并确保全人类的繁荣。此人的工作有助于实现下列可持续发展目标：

指纹图谱

深入其中 Xiumin Shi 为活跃的研究主题。这些主题标签来自此人的成果。它们共同形成唯一的指纹。

1 相似简介

17 会议稿件
12 文章

A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks
Song, Y., Zhou, H., Peng, J., Wang, L. & Shi, X., 2024, Fourth International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024. Piccaluga, P. P., El-Hashash, A. & Guo, X. (编辑). SPIE, 132521Q. (Proceedings of SPIE - The International Society for Optical Engineering; 卷 13252).
科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审
Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network
Qin, H., Shi, X., Zhu, Y., Ma, J., Deng, X. & Wang, L., 7月 2024, 在: European Journal of Neuroscience. 60, 2, 页码 4034-4048 15 页码
科研成果: 期刊稿件 › 文章 › 同行评审
1 引用（Scopus）
DL_BBBP: Blood-brain barrier permeability prediction based on molecular property using deep learning
Sun, Y., Zhou, H., Wen, Z., Liang, C., Tang, J., Wang, L. & Shi, X., 2024, Fourth International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024. Piccaluga, P. P., El-Hashash, A. & Guo, X. (编辑). SPIE, 132521P. (Proceedings of SPIE - The International Society for Optical Engineering; 卷 13252).
科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审
MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction
Li, X., Shi, X., Li, Y. & Wang, L., 1 9月 2024, 在: Journal of integrative bioinformatics. 21, 3
科研成果: 期刊稿件 › 文章 › 同行评审

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MoSwformer: A Transformer-based patient classification model using multi-omics data
Liu, M., Zhu, Y., Li, X., Shi, X. & Wang, L., 2024, Fourth International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024. Piccaluga, P. P., El-Hashash, A. & Guo, X. (编辑). SPIE, 1325216. (Proceedings of SPIE - The International Society for Optical Engineering; 卷 13252).
科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

石秀民

个人简介

与联合国可持续发展目标相关的专业知识

指纹图谱

最近五年的合作关系和顶尖研究领域

查看详情

A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network

DL_BBBP: Blood-brain barrier permeability prediction based on molecular property using deep learning

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction

MoSwformer: A Transformer-based patient classification model using multi-omics data

石 秀民

个人简介

与联合国可持续发展目标相关的专业知识

指纹图谱

最近五年的合作关系和顶尖研究领域

科研成果

A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network

DL_BBBP: Blood-brain barrier permeability prediction based on molecular property using deep learning

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction

MoSwformer: A Transformer-based patient classification model using multi-omics data

石秀民