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高斯过程先验贝叶斯数据融合:一个应用于蛋白质折叠识别

Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition
课程网址: http://videolectures.net/pmsb06_girolami_apfr/  
主讲教师: Mark Girolami
开课单位: 格拉斯哥大学
开课时间: 2007-02-25
课程语种: 英语
中文简介:
各种新兴的定量测量技术正在产生基因组,转录组和蛋白质组范围的数据集合,这促使数据集成方法在推理框架内的发展。已经证明,对于计算生物学中的某些预测任务,可以通过集成许多(可能是异构的)数据源来获得性能的协同改进。在[1]中,使用支持向量机(SVM)将蛋白质的六种不同参数表示用于蛋白质的倍数识别。
课程简介: Various emerging quantitative measurement technologies are producing genome, transcriptome and proteome-wide data collections which has motivated the de- velopment of data integration methods within an inferential framework. It has been demonstrated that for certain prediction tasks within computational biol- ogy synergistic improvements in performance can be obtained via integration of a number of (possibly heterogeneous) data sources. In [1] six different parameter representations of proteins were employed for fold recognition of proteins using Support Vector Machines (SVM).
关 键 词: 定量测量技术; 计算生物学; 蛋白质折叠识别
课程来源: 视频讲座网
最后编审: 2020-10-14:yumf
阅读次数: 58