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1. 发明申请

US20140066320A1 IDENTIFYING CAUSAL GENETIC MARKERS FOR A SPECIFIED PHENOTYPE 审中-公开
标题翻译：鉴定指定基因的原始基因标记
公开(公告)号：US20140066320A1
公开(公告)日：2014-03-06
申请号：US13603403
申请日：2012-09-04
申请人： David Earl Heckerman , Jennifer Listgarten , Christoph Anthony Lippert , Jing Xiang , Nicolo Fusi , Carl M. Kadie , Robert I. Davidson
发明人： David Earl Heckerman , Jennifer Listgarten , Christoph Anthony Lippert , Jing Xiang , Nicolo Fusi , Carl M. Kadie , Robert I. Davidson
IPC分类号： C40B30/02
CPC分类号： G16B20/00 , G16B40/00
摘要： Described herein are technologies pertaining to computationally-efficiently performing genome-wide association studies. Feature selection methods are used to identify genetic markers for addressing potential confounding in the data. Then, single SNPs, or groups of genetic markers are analyzed to ascertain whether such groups are causal or tagging of causal as to a specified phenotype, after taking in to account the feature-selected SNPs. Group and univariate analysis is accomplished by way of analyzing a group of genetic markers conditioned upon other genetic markers that are found to be predictive of the specified phenotype.
摘要翻译：这里描述的是涉及计算上有效执行全基因组关联研究的技术。特征选择方法用于识别用于解决数据中潜在混淆的遗传标记。然后分析单核苷酸多态性（SNPs）或遗传标记物组，以确定这些群体在考虑特征选择的单核苷酸多态性之后，是否因为特定表型的因果性或标记因素。组和单变量分析是通过分析一组基于其他遗传标记的遗传标记来实现的，这些遗传标记被发现是预测指定的表型。

2. 发明授权

US08473218B2 Refining HLA data 有权
标题翻译：精炼HLA数据
公开(公告)号：US08473218B2
公开(公告)日：2013-06-25
申请号：US12431786
申请日：2009-04-29
申请人： Jennifer Listgarten , David Earl Heckerman , Carl M. Kadie
发明人： Jennifer Listgarten , David Earl Heckerman , Carl M. Kadie
IPC分类号： G01N33/50
CPC分类号： G06F19/18 , G06F19/24
摘要： A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.
摘要翻译：本文描述的系统包括接收HLA数据集的接收器组件，其中HLA数据集包括低分辨率HLA数据。 HLA细化组件包括自动细化HLA数据集以将低分辨率HLA数据转换为高分辨率HLA数据的统计模型。

3. 发明申请

US20100191513A1 REFINING HLA DATA 有权
标题翻译：修改HLA数据
公开(公告)号：US20100191513A1
公开(公告)日：2010-07-29
申请号：US12431786
申请日：2009-04-29
申请人： Jennifer Listgarten , David Earl Heckerman , Carl M. Kadie
发明人： Jennifer Listgarten , David Earl Heckerman , Carl M. Kadie
IPC分类号： G06F17/10 , G06F17/18
CPC分类号： G06F19/18 , G06F19/24
摘要： A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.
摘要翻译：本文描述的系统包括接收HLA数据集的接收器组件，其中HLA数据集包括低分辨率HLA数据。 HLA细化组件包括自动细化HLA数据集以将低分辨率HLA数据转换为高分辨率HLA数据的统计模型。

4. 发明申请

US20130246033A1 PREDICTING PHENOTYPES OF A LIVING BEING IN REAL-TIME 审中-公开
标题翻译：实时预测生活中的阴谋
公开(公告)号：US20130246033A1
公开(公告)日：2013-09-19
申请号：US13419439
申请日：2012-03-14
申请人： David Earl Heckerman , Jennifer Listgarten , Carl M. Kadie , Omer Weissbrod
发明人： David Earl Heckerman , Jennifer Listgarten , Carl M. Kadie , Omer Weissbrod
IPC分类号： G06G7/60
CPC分类号： G16B20/00 , G16B40/00
摘要： Described herein are technologies pertaining to predicting whether a living being, such as a human being, an animal, or a plant, has a phenotype or set of phenotypes in real-time or near real-time. A filter set of genetic markers are determined heuristically, by first univariately computing scores for respective genetic markers that are indicative of their predictive ability with respect to the phenotype or the set of phenotypes. Thereafter, during training, the filter set is initially selected and thereafter expanded based upon the scores, until predictive accuracy for the phenotype or set of phenotypes reaches a threshold or is optimized. The filter set, which includes a relatively small number of genetic markers, is subsequently employed for real-time or near-real time phenotype prediction.
摘要翻译：这里描述的是关于预测诸如人类，动物或植物的生物是否具有实时或接近实时的表型或一组表型的技术。初步确定遗传标记的过滤器集合，首先单个计算相应遗传标记的分数，这些评估指示其相对于表型或一组表型的预测能力。此后，在训练期间，最初选择过滤器组，然后基于分数进行扩展，直到表型或一组表型的预测精度达到阈值或被优化为止。随后将包括相对较少数量的遗传标记的滤器组用于实时或近实时表型预测。

5. 发明申请

US20130246017A1 COMPUTING PARAMETERS OF A PREDICTIVE MODEL 审中-公开
标题翻译：预测模型的计算参数
公开(公告)号：US20130246017A1
公开(公告)日：2013-09-19
申请号：US13549527
申请日：2012-07-16
申请人： David Earl Heckerman , Jennifer Listgarten , Carl M. Kadie , Omer Weissbrod
发明人： David Earl Heckerman , Jennifer Listgarten , Carl M. Kadie , Omer Weissbrod
IPC分类号： G06F17/10
CPC分类号： G06K9/6278 , H04L51/12
摘要： A computer-executable algorithm that estimates parameters of a predictive model in computation time of less than O(n2k2) when k
摘要翻译：本文描述了当k <= n时估计小于O（n2k2）的计算时间中的预测模型的参数的计算机可执行算法，其中n是在估计预测模型的参数时考虑的数量项数， k是在估计预测模型的参数时考虑的每个数据项的多个特征。在给定训练数据中考虑的特征的情况下，估计这些参数最大化训练数据中观察目标值的概率。

6. 发明申请

US20090106172A1 FALSE DISCOVERY RATE FOR GRAPHICAL MODLES 失效
标题翻译：图形模型的虚拟发现率
公开(公告)号：US20090106172A1
公开(公告)日：2009-04-23
申请号：US11873440
申请日：2007-10-17
申请人： David E. Heckerman , Jennifer Listgarten , Carl M. Kadie
发明人： David E. Heckerman , Jennifer Listgarten , Carl M. Kadie
IPC分类号： G06F15/18
CPC分类号： G06N7/005
摘要： The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.
摘要翻译：所要求保护的主题提供确定与学习的图形模型相关联的多个非虚假弧的系统和/或方法。该系统可以包括利用学习算法和数据集来生成学习图形模型和与数据集的零排列相关联的图形模型的装置和机制，确定与与数据集的零排列相关联的图形模型相关联的平均弧数，列举与所学习的图形模型相关联的弧的总数，并且呈现平均弧数与总弧数的比率，该比率表示与所学习的图形模型相关联的非虚假弧的数量。

7. 发明申请

US20080172215A1 T-CELL EPIOTOPE PREDICTION 有权
标题翻译： T细胞EPIOTOPE预测
公开(公告)号：US20080172215A1
公开(公告)日：2008-07-17
申请号：US11963081
申请日：2007-12-21
申请人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
发明人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
IPC分类号： G06G7/60
CPC分类号： G06F19/24 , G06F19/16 , G06F19/18
摘要： Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.
摘要翻译：本文描述了表位预测模型。作为示例，用于预测与表位相关的表位信息的系统可以包括分类模型（例如逻辑回归模型）。经训练的分类模型可以说明性地操作地对所接收的蛋白质数据执行一个矿物物流功能，并且包含一个或多个隐藏的二进制变量和移位变量，其在被处理时表示一个或多个所需表位的识别（例如，预测）。分类模型可以被配置为通过处理包括表位，MHC，MHC超类型和布尔组合的各种特征的数据来预测表位信息。

8. 发明授权

US07885905B2 False discover rate for graphical models 失效
标题翻译：图形模型的假发现率
公开(公告)号：US07885905B2
公开(公告)日：2011-02-08
申请号：US11873440
申请日：2007-10-17
申请人： David E Heckerman , Jennifer Listgarten , Carl M Kadie
发明人： David E Heckerman , Jennifer Listgarten , Carl M Kadie
IPC分类号： G06F15/18
CPC分类号： G06N7/005
摘要： The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.
摘要翻译：所要求保护的主题提供确定与学习的图形模型相关联的多个非虚假弧的系统和/或方法。该系统可以包括利用学习算法和数据集来生成学习图形模型和与数据集的零排列相关联的图形模型的装置和机制，确定与与数据集的零排列相关联的图形模型相关联的平均弧数，列举与所学习的图形模型相关联的弧的总数，并且呈现平均弧数与总弧数的比率，该比率表示与所学习的图形模型相关联的非虚假弧的数量。

9. 发明授权

US08121797B2 T-cell epitope prediction 有权
标题翻译： T细胞表位预测
公开(公告)号：US08121797B2
公开(公告)日：2012-02-21
申请号：US11963081
申请日：2007-12-21
申请人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
发明人： David E. Heckerman , Carl M. Kadie , Jennifer Listgarten , Noah Aaron Zaitlen , Nebojsa Jojic
IPC分类号： G01N33/50
CPC分类号： G06F19/24 , G06F19/16 , G06F19/18
摘要： Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.
摘要翻译：本文描述了表位预测模型。作为示例，用于预测与表位相关的表位信息的系统可以包括分类模型（例如逻辑回归模型）。经训练的分类模型可以说明性地操作地对所接收的蛋白质数据执行一个矿物物流功能，并且包含一个或多个隐藏的二进制变量和移位变量，其在被处理时表示一个或多个所需表位的识别（例如，预测）。分类模型可以被配置为通过处理包括表位，MHC，MHC超类型和布尔组合的各种特征的数据来预测表位信息。

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