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题名: Force curve classification using independent component analysis and support vector machine
作者: Zhou FY(周富元); Wang WX(王文学); Li M(李密); Liu LQ(刘连庆)
作者部门: 机器人学研究室
会议名称: 9th IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED 2015
会议日期: November 15-18, 2015
会议地点: Honolulu, HI, USA
会议主办者: Huawei Technologies Co., Ltd.; IEEE Nanotechnology Council; RSC Advances of the Royal Society of Chemistry; Shenzhen Academy of Robotics; University of Arkansas; University of California at Santa Cruz
会议录: 9th IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED 2015
会议录出版者: IEEE Computer Society
会议录出版地: Washington, DC
出版日期: 2015
页码: 167-172
收录类别: EI
ISSN号: 2159-6964
ISBN号: 9781467396714
摘要: The development of single-molecule force spectroscopy (SMFS) technique, especially the atomic force microscope (AFM) based SMFS technique, has been widely applied to the studies of receptor-ligand at single-cell and single-molecule level and has greatly enhanced the understanding of biological activity like the drug action on the cells. The studies have shown that three types of acting forces between proteins and ligands, specific binding, non-specific binding, and non-interaction, can be distinguished manually according to the characteristics of force curves for further analysis. However the efficiency of manual classification of such force curves is low and results in difficulty in analyzing large set of experimental data. In this study, we demonstrate a machine learning based approach to automatic classification of the three types of force curves and a low pass filter for noise removal, independent component analysis for dimensionality reduction and support vector machine for data classification are involved in this process. It is validated by the experiments that the three types of force curves recorded using AFM can be effectively and efficiently classified with the proposed approach.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/18758
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
Zhou FY,Wang WX,Li M,et al. Force curve classification using independent component analysis and support vector machine[C]. 见:9th IEEE International Conference on Nano/Molecular Medicine and Engineering, NANOMED 2015. Honolulu, HI, USA. November 15-18, 2015.
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文件名: Force curve classification using independent component analysis and support vector machine.pdf
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