The 2013 6th International Congress on Image and Signal Processing(CISP 2013)and the 2013 6th International Conference on BioMedical Engineering and Informatics(BMEI 2013)will be jointly held in Hangzhou,China.CISP-BM...The 2013 6th International Congress on Image and Signal Processing(CISP 2013)and the 2013 6th International Conference on BioMedical Engineering and Informatics(BMEI 2013)will be jointly held in Hangzhou,China.CISP-BMEI2013 is a premier international forum for scientists and researchers to present the state-of-the-art of multimedia,signal processing,biomedical engineering and informatics and to discuss future research challenges.Co-locating two conferences we aim to promote collaboration among multiple areas,especially the interactions of engineering,computing,and medicine.CISP’13-BMEI'13 is technically co-sponsored by the IEEE Engineering in Medicine and Biology Society.展开更多
基因-疾病关联关系预测已经成为当前生物医学研究的一个热点。现有的关联预测方法通常会遭受基因-疾病关联数据稀疏和PU(positive and unlabeled)问题的影响。基于以上不足,提出一种基于Katz增强归纳型矩阵补全的基因-疾病关联预测模型...基因-疾病关联关系预测已经成为当前生物医学研究的一个热点。现有的关联预测方法通常会遭受基因-疾病关联数据稀疏和PU(positive and unlabeled)问题的影响。基于以上不足,提出一种基于Katz增强归纳型矩阵补全的基因-疾病关联预测模型。该模型由基于Katz方法的预估计和基于归纳型矩阵补全方法的精化估计两个步骤组成。具体地,先利用Katz方法基于基因-疾病异构网络对基因-疾病关联进行预估计,以期缓解关联数据稀疏和PU问题的影响。然而,受制于相似度网络的质量,Katz方法在预估计基因-疾病关联时不可避免地会引入一些噪声,为此,将弹性网正则化技术引入传统的归纳型矩阵补全模型以增强其鲁棒性,进而用改进的归纳型矩阵补全模型来精化基因-疾病关联预测效果。实验结果表明,与目前流行的基因-疾病关联预测方法相比,所提出的模型在查全率和查准率上均有显著提高,同时也能解决关联预测中常见的冷启动问题。展开更多
文摘The 2013 6th International Congress on Image and Signal Processing(CISP 2013)and the 2013 6th International Conference on BioMedical Engineering and Informatics(BMEI 2013)will be jointly held in Hangzhou,China.CISP-BMEI2013 is a premier international forum for scientists and researchers to present the state-of-the-art of multimedia,signal processing,biomedical engineering and informatics and to discuss future research challenges.Co-locating two conferences we aim to promote collaboration among multiple areas,especially the interactions of engineering,computing,and medicine.CISP’13-BMEI'13 is technically co-sponsored by the IEEE Engineering in Medicine and Biology Society.
文摘基因-疾病关联关系预测已经成为当前生物医学研究的一个热点。现有的关联预测方法通常会遭受基因-疾病关联数据稀疏和PU(positive and unlabeled)问题的影响。基于以上不足,提出一种基于Katz增强归纳型矩阵补全的基因-疾病关联预测模型。该模型由基于Katz方法的预估计和基于归纳型矩阵补全方法的精化估计两个步骤组成。具体地,先利用Katz方法基于基因-疾病异构网络对基因-疾病关联进行预估计,以期缓解关联数据稀疏和PU问题的影响。然而,受制于相似度网络的质量,Katz方法在预估计基因-疾病关联时不可避免地会引入一些噪声,为此,将弹性网正则化技术引入传统的归纳型矩阵补全模型以增强其鲁棒性,进而用改进的归纳型矩阵补全模型来精化基因-疾病关联预测效果。实验结果表明,与目前流行的基因-疾病关联预测方法相比,所提出的模型在查全率和查准率上均有显著提高,同时也能解决关联预测中常见的冷启动问题。
