M-NET:将单核苷酸变异转化为患者特征图像,以预测前列腺癌转移并识别重要途径。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Li Zhou, Jie Li, Weilong Tan
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引用次数: 0

摘要

基于单核苷酸变异对前列腺癌转移进行高性能预测仍是一项挑战。因此,我们开发了一种新颖的生物学深度学习框架,名为 M-NET,用于预测前列腺癌转移。在该框架内,我们将单核苷酸变异转化为患者特征图像,这些图像是拟合卷积神经网络的最佳图像。此外,我们还确定了与转移状态相关的重要通路。实验结果表明,M-NET明显优于其他基于单核苷酸变异的比较方法,在准确率、精确度、召回率、F1-分数、接收者操作特征曲线下面积和精确度-召回率曲线下面积方面分别提高了6.3%、8.4%、5.1%、0.070、0.041和0.026。此外,M-NET 还发现了一些与转移状态相关的重要通路,如刺猬通路信号转导。总之,与其他比较方法相比,M-NET 在预测前列腺癌转移方面表现出更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
M-NET: Transforming Single Nucleotide Variations into Patient Feature Images for the Prediction of Prostate Cancer Metastasis and Identification of Significant Pathways.

High-performance prediction of prostate cancer metastasis based on single nucleotide variations remains a challenge. Therefore, we developed a novel biologically informed deep learning framework, named M-NET, for the prediction of prostate cancer metastasis. Within the framework, we transformed single nucleotide variations into patient feature images that are optimal for fitting convolutional neural networks. Moreover, we identified significant pathways associated with the metastatic status. The experimental results showed that M-NET significantly outperformed other comparison methods based on single nucleotide variations, achieving improvements in accuracy, precision, recall, F1-score, area under the receiver operating characteristics curve, and area under the precision-recall curve by 6.3%, 8.4%, 5.1%, 0.070, 0.041, and 0.026, respectively. Furthermore, M-NET identified some important pathways associated with the metastatic status, such as signaling by the hedgehog pathway. In summary, compared with other comparative methods, M-NET exhibited a better performance in the prediction of prostate cancer metastasis.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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