生物标志物发现的计算方法

M. Yousef, N. Najami, Loai AbedAllah, Waleed Khalifa
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引用次数: 9

摘要

计算生物学在发现新的生物标志物、分析疾病状态和验证潜在生物标志物方面发挥着重要作用。生物标志物用于测量疾病的进展或治疗干预在疾病治疗中的生理效应。它们还被用作癌症和炎性疾病等各种疾病的早期预警信号。本文综述了近年来计算生物学在生物标志物发现研究中的应用进展。简要讨论了生物标记物发现中常用的机器学习技术(如聚类和支持向量机)的一些必要的基础知识,然后描述了生物标记物的生物学背景。我们进一步研究计算生物学方法和生物标志物的整合。最后,我们总结了计算生物学对生物标志物发现的关键挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational Approaches for Biomarker Discovery
Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Biomarkers are used to measure the progress of disease or the physiological effects of therapeutic intervention in the treatment of disease. They are also used as early warning signs for various diseases such as cancer and inflammatory diseases. In this review, we outline recent progresses of computational biology application in research on biomarkers discovery. A brief discussion of some necessary preliminaries on machine learning techniques (e.g., clustering and support vector machines—SVM) which are commonly used in many applications to biomarkers discovery is given and followed by a description of biological background on biomarkers. We further examine the integration of computational biology approaches and biomarkers. Finally, we conclude with a discussion of key challenges for computational biology to biomarkers discovery.
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