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1. 发明申请

US20070192039A1 SHIFT-INVARIANT PREDICTIONS 有权
标题翻译：移位不变预测
公开(公告)号：US20070192039A1
公开(公告)日：2007-08-16
申请号：US11738411
申请日：2007-04-20
申请人： Nebojsa Jojic , David Heckerman , Noah Zaitlen , Manuel Gomez
发明人： Nebojsa Jojic , David Heckerman , Noah Zaitlen , Manuel Gomez
IPC分类号： G06F19/00
CPC分类号： G06F19/18 , G06F19/16 , G06F19/24
摘要： Shift invariant predictors are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.
摘要翻译：这里描述了移位不变量预测器。作为示例，用于预测与蛋白质和配体的结合相关的结合信息的系统可以包括训练的结合模型和预测组分。经训练的结合模型可以包括表示蛋白质结合位点处配体的未知比对的隐藏变量。预测组件可以被配置为通过使用关于蛋白质序列，配体序列和训练的结合模型的信息来预测结合信息。

2. 发明申请

US20070192033A1 Molecular interaction predictors 审中-公开
标题翻译：分子相互作用预测因子
公开(公告)号：US20070192033A1
公开(公告)日：2007-08-16
申请号：US11356196
申请日：2006-02-16
申请人： Nebojsa Jojic , Manuel Gomez
发明人： Nebojsa Jojic , Manuel Gomez
IPC分类号： G06F19/00
CPC分类号： G16B40/00 , G16B15/00
摘要： Adaptive threading models for predicting an interaction between two or more molecules such as proteins are provided. The adaptive threading models have one or more learnable parameters that can be learned from all or some of the available data. The available data can include data relating to known interactions between the two or more molecules, the composition of the molecules and the geometry of the molecular complex.
摘要翻译：提供了用于预测两个或多个分子（例如蛋白质）之间的相互作用的自适应穿线模型。自适应线程模型具有一个或多个可学习的参数，可从全部或部分可用数据中学习。可用的数据可以包括与两个或更多个分子之间的已知相互作用，分子的组成和分子复合物的几何形状有关的数据。

3. 发明申请

US20080021686A1 CLUSTER MODELING, AND LEARNING CLUSTER SPECIFIC PARAMETERS OF AN ADAPTIVE DOUBLE THREADING MODEL 有权
标题翻译：自适应双线性模型的聚类建模和学习聚类特定参数
公开(公告)号：US20080021686A1
公开(公告)日：2008-01-24
申请号：US11770684
申请日：2007-06-28
申请人： Nebojsa Jojic , David Heckerman , Manuel Jesus Reyes Gomez
发明人： Nebojsa Jojic , David Heckerman , Manuel Jesus Reyes Gomez
IPC分类号： G06G7/48
CPC分类号： G06F19/24 , G06F19/16 , G06G7/48
摘要： Cluster models are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a probability distribution and a hidden variable that represents a cluster of protein sequences, and/or a set of hidden variables representing learned supertypes. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.
摘要翻译：这里描述了群集模型。作为示例，用于预测与蛋白质和配体的结合相关的结合信息的系统可以包括训练的结合模型和预测组分。经训练的绑定模型可以包括概率分布和表示蛋白质序列簇的隐藏变量，和/或表示学习超类型的一组隐藏变量。预测组件可以被配置为通过使用关于蛋白质序列，配体序列和训练的结合模型的信息来预测结合信息。

4. 发明申请

US20070112598A1 TOOLS FOR HEALTH AND WELLNESS 有权
标题翻译：健康和健康工具
公开(公告)号：US20070112598A1
公开(公告)日：2007-05-17
申请号：US11556085
申请日：2006-11-02
申请人： David Heckerman , Craig Mundie , Nebojsa Jojic , Randy Hinrichs
发明人： David Heckerman , Craig Mundie , Nebojsa Jojic , Randy Hinrichs
IPC分类号： G06Q10/00 , G06F7/00 , G06Q99/00
CPC分类号： G06N99/005 , G06F19/00 , G06Q10/10 , G06Q50/22 , G16H50/20 , G16H50/70
摘要： A tool for providing health and/or wellness services is described herein. Not necessarily clean or unclean data about a plurality of self-selected or non-selected or unselected subjects is received. The data can be aggregated and mined at least in part by employing a statistical algorithm, a data-mining algorithm and/or a machine-learning algorithm. The data can be further employed to provide health and/or wellness services to participants.
摘要翻译：本文描述了用于提供健康和/或健康服务的工具。不一定接收到关于多个自选或未选择的对象的干净或不洁净的数据。数据可以至少部分地通过采用统计算法，数据挖掘算法和/或机器学习算法进行聚合和挖掘。数据可以进一步用于向参与者提供健康和/或健康服务。

5. 发明申请

US20070112597A1 MONETIZING LARGE-SCALE INFORMATION COLLECTION AND MINING 审中-公开
标题翻译：制作大规模信息采集和采矿
公开(公告)号：US20070112597A1
公开(公告)日：2007-05-17
申请号：US11556069
申请日：2006-11-02
申请人： David Heckerman , Craig Mundie , Nebojsa Jojic , Randy Hinrichs
发明人： David Heckerman , Craig Mundie , Nebojsa Jojic , Randy Hinrichs
IPC分类号： G06Q10/00 , G07G1/14 , G06F7/00
CPC分类号： G06Q30/0273 , G06N20/00 , G06Q30/0201 , G06Q30/0203 , G06Q30/0207 , G06Q30/0256 , G06Q30/0277 , G06Q50/22
摘要： The subject matter described herein facilitates monetizing a database of unclean health-related data collected on a large-scale and pertaining to a non-selected population. At least one pattern can be automatically ascertained from the unclean health-related data at least in part by applying a statistical technique, a data mining technique and/or a machine-learning technique to the database. The use of the database can be tracked and fees determined accordingly.
摘要翻译：本文描述的主题有助于通过大规模收集的和与非选择群体有关的不洁净的健康相关数据的数据库进行货币化。至少一部分模式可以至少部分地通过对数据库应用统计技术，数据挖掘技术和/或机器学习技术从不洁净的健康相关数据自动确定。可以跟踪使用数据库，并相应地确定费用。

6. 发明申请

US20060160071A1 Association-based predictions 有权
标题翻译：基于协会的预测
公开(公告)号：US20060160071A1
公开(公告)日：2006-07-20
申请号：US11324634
申请日：2005-12-30
申请人： David Heckerman , Simon Mallal , Carl Kadie , Corey Moore , Nebojsa Jojic
发明人： David Heckerman , Simon Mallal , Carl Kadie , Corey Moore , Nebojsa Jojic
IPC分类号： C12Q1/70 , G06F19/00
CPC分类号： G06F19/22 , C12Q1/6883 , C12Q2537/165 , G06F19/00 , G06F19/14 , G06F19/18 , G06F19/24 , G16H50/20 , Y02A90/26
摘要： A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism's condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism's condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.
摘要翻译：这里描述了一种系统，其包括由主机和病原体之间的多个关联训练的机器学习分类器来预测病原体特征。病原体特征可以与宿主的疾病状态有关。本文还描述了用于执行预测影响生物体状况的目标分子的一部分的预测方法的计算机可执行指令。该方法包括使用群体数据来自动分析目标分子的一个或多个区域以确定预期影响生物体状况的目标分子的部分。人口数据可以涉及至少一种不同的生物性状和目标分子之间的至少一种关系。也考虑了采用该方法预测的一个或多个表位。

7. 发明申请

US20060160070A1 Association-based epitome design 审中-公开
标题翻译：基于协会的缩影设计
公开(公告)号：US20060160070A1
公开(公告)日：2006-07-20
申请号：US11324467
申请日：2005-12-30
申请人： Simon Mallal , David Heckerman , Nebojsa Jojic , Vladimir Jojic , Christopher Meek , Corey Moore , Carl Kadie
发明人： Simon Mallal , David Heckerman , Nebojsa Jojic , Vladimir Jojic , Christopher Meek , Corey Moore , Carl Kadie
IPC分类号： C12Q1/70 , G06F19/00
CPC分类号： G16B40/00 , G16B10/00 , G16B20/00 , G16B30/00
摘要： Systems that facilitate immunogen design are described herein. An optimization component is provided to determine an immunogen according to at least one criterion. The immunogen comprises a set of overlapping sequences comprising sequences that are known to be and/or are likely to be immunogenic. At least one of the sequences that are likely to be immunogenic can be determined by analyzing associations between a host and a pathogen at a population level. Methods of determining an epitome are described herein. A plurality of sequences are received. At least one of the sequences is predicted to be an epitope based on a relationship between a diverse trait of a population and a mutation of a pathogen. A collection of the plurality of sequences is optimized according to one or more criteria to determine the epitome. Epitomes and immunogens determined by the systems and methods described herein are also contemplated.
摘要翻译：本文描述了促进免疫原设计的系统。提供优化组件以根据至少一个标准确定免疫原。免疫原包含一组重叠序列，其包含已知是和/或可能是免疫原性的序列。可能通过在群体水平上分析宿主和病原体之间的关联来确定可能是免疫原性的序列中的至少一个。本文描述了确定缩影的方法。接收多个序列。基于群体的不同性状和病原体的突变之间的关系，至少有一个序列被预测为表位。根据一个或多个标准来优化多个序列的集合以确定缩写。还考虑了通过本文所述的系统和方法确定的病原体和免疫原。

8. 发明申请

US20070106626A1 Large-scale information collection and mining 有权
标题翻译：大型信息采集和挖掘
公开(公告)号：US20070106626A1
公开(公告)日：2007-05-10
申请号：US11266974
申请日：2005-11-04
申请人： Craig Mundie , David Heckerman , Nebojsa Jojic , Randy Hinrichs
发明人： Craig Mundie , David Heckerman , Nebojsa Jojic , Randy Hinrichs
IPC分类号： G06N3/02
CPC分类号： G06N99/005 , G06F19/00 , G06Q10/10 , G06Q50/24 , G16H50/20
摘要： The methods/systems described herein facilitate large-scale data collection and aggregation. One exemplary system that facilitates large-scale reporting of health-related data comprises a data collection component, a database and an aggregation component. The data collection component can collect health-related data on a large-scale from a non-selected population. The database can store at least some of the health-related data. The aggregation component can facilitate automatically ascertaining at least one pattern from the health-related data at least in part by applying one or more statistical, data-mining or machine-learning techniques to the database. One exemplary method of extracting health observations from information obtained on a macro-scale comprises receiving information about a plurality of self-selected subjects, pooling the information, mining the pooled information at least in part by employing a data-mining algorithm to infer one or more health observations from the pooled information, and monetizing the one or more health observations.
摘要翻译：这里描述的方法/系统有助于大规模数据收集和聚合。促进健康相关数据的大规模报告的一个示例性系统包括数据收集组件，数据库和聚合组件。数据收集组件可以从非选定人群大规模收集健康相关数据。数据库可以存储至少一些健康相关数据。聚合组件可以至少部分地通过将一个或多个统计，数据挖掘或机器学习技术应用于数据库来自动地从健康相关数据确定至少一个模式。从在宏观尺度上获得的信息中提取健康观测的一个示例性方法包括：接收关于多个自选择对象的信息，汇集信息，至少部分地通过采用数据挖掘算法推断出一个或多个从汇集的信息中获得更多的健康意见，并通过一个或多个健康观察获利。

9. 发明申请

US20060190226A1 Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails 审中-公开
公开(公告)号：US20060190226A1
公开(公告)日：2006-08-24
申请号：US11324691
申请日：2005-12-30
申请人： Nebojsa Jojic , Vladimir Jojic , David Heckerman , Brendan Frey , Christopher Meek
发明人： Nebojsa Jojic , Vladimir Jojic , David Heckerman , Brendan Frey , Christopher Meek
IPC分类号： G06G7/48 , A61K39/21
CPC分类号： G16B40/00 , G16B10/00 , G16B20/00 , G16B30/00
摘要： The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

10. 发明申请

US20060178861A1 Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails 有权
公开(公告)号：US20060178861A1
公开(公告)日：2006-08-10
申请号：US11324506
申请日：2005-12-30
申请人： Nebojsa Jojic , Vladimir Jojic , David Heckerman , Brendan Frey , Christopher Meek
发明人： Nebojsa Jojic , Vladimir Jojic , David Heckerman , Brendan Frey , Christopher Meek
IPC分类号： G06G7/48 , G06G7/58
CPC分类号： G06F19/22 , G06F19/14 , G06F19/18 , G06F19/24 , G06G7/48 , G06G7/58
摘要： The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

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