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1. 发明申请

US20060046256A1 Identification of informative genetic markers 审中-公开
标题翻译：识别信息遗传标记
公开(公告)号：US20060046256A1
公开(公告)日：2006-03-02
申请号：US11040652
申请日：2005-01-21
申请人： Bjarni Halldorsson , Vineet Bafna , Ross Lippert , Russell Schwartz , Francisco De La Vega , Andrew Clark , Sorin Istrail
发明人： Bjarni Halldorsson , Vineet Bafna , Ross Lippert , Russell Schwartz , Francisco De La Vega , Andrew Clark , Sorin Istrail
IPC分类号： C12Q1/68 , G06F19/00
CPC分类号： G16B20/00
摘要： The present teachings describe methods for selecting informative genetic markers including single nucleotide polymorphisms (SNPs) that may be used in the design and execution of genome wide association studies. These methods are distinguished from other methods relying on a predefined haplotype block structure and may be configured to make use of correlations that occur across neighboring haplotype blocks. The disclosed methods may further be implemented across chromosomal regions having both high and low local linkage disequilibrium. Informative genetic marker selection, as described, provides an alternative and potentially more efficient mechanism to select genetic markers such as SNPs using block-based and random approaches.
摘要翻译：本教导描述了用于选择可用于设计和执行基因组广泛关联研究的信息性遗传标记（包括单核苷酸多态性（SNP））的方法。这些方法与依赖于预定单倍型块结构的其它方法不同，并且可以被配置为利用在相邻单倍型块之间发生的相关性。所公开的方法可以进一步在具有高和低局部连锁不平衡的染色体区域上实现。如上所述，信息性遗传标记选择提供了使用基于块和随机方法选择遗传标记例如SNP的替代和潜在更有效的机制。

2. 发明授权

US07685080B2 Regularized least squares classification or regression with leave-one-out (LOO) error 失效
标题翻译：正则化最小二乘法分类或回归与离开（LOO）错误
公开(公告)号：US07685080B2
公开(公告)日：2010-03-23
申请号：US11535921
申请日：2006-09-27
申请人： Ryan Rifkin , Ross Lippert
发明人： Ryan Rifkin , Ross Lippert
IPC分类号： G06E1/00
CPC分类号： G06K9/6215 , G06K9/6256 , G06K9/6269 , G06N99/005
摘要： Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier/regression algorithm. The techniques include a classification/regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification/regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.
摘要翻译：公开了一种实现用于在正则化参数λ的大量值上快速找到正则化最小二乘（RLS）问题的离开一（LOO）误差的算法。实现这些技术的算法使用与训练单个正则化最小二乘法分类器/回归算法大致相同的时间和空间。这些技术包括适用于中等尺寸数据集的分类/回归过程，基于非规则化核心矩阵的特征分解。该过程被应用于许多基准数据集，从经验上可以看出，可以使用具有令人惊讶的较大带宽参数值的高斯核进行准确的分类/回归。进一步证明了如何利用这个大型的获得线性时间算法，适用于计算LOO值并扫过λ的大数据集。

3. 发明申请

US20070094180A1 REGULARIZED LEAST SQUARES CLASSIFICATION/REGRESSION 失效
标题翻译：常规最小二乘法分类/回归
公开(公告)号：US20070094180A1
公开(公告)日：2007-04-26
申请号：US11535921
申请日：2006-09-27
申请人： Ryan Rifkin , Ross Lippert
发明人： Ryan Rifkin , Ross Lippert
IPC分类号： G06F15/18
CPC分类号： G06K9/6215 , G06K9/6256 , G06K9/6269 , G06N99/005
摘要： Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier/regression algorithm. The techniques include a classification/regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification/regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.
摘要翻译：公开了一种实现用于在正则化参数λ的大量值上快速找到正则化最小二乘（RLS）问题的离开一（LOO）误差的算法。实现这些技术的算法使用与训练单个正则化最小二乘法分类器/回归算法大致相同的时间和空间。这些技术包括适用于中等尺寸数据集的分类/回归过程，基于非规则化核心矩阵的特征分解。这个过程被应用于许多基准数据集，从经验上可以看出，使用具有惊人的大值带宽参数sigma的高斯核可以执行准确的分类/回归。进一步证明了如何利用这个大的sigma方案获得一个线性时间算法，适用于计算LOO值并扫过λ的大型数据集。

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