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1. 发明授权

US08478535B2 Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails 有权
标题翻译：利用机器学习算法方便装配疫苗鸡尾酒的系统和方法
公开(公告)号：US08478535B2
公开(公告)日：2013-07-02
申请号：US11324506
申请日：2005-12-30
申请人： Nebojsa Jojic , Vladimir Jojic , David E. Heckerman , Brendan John Frey , Christopher A. Meek
发明人： Nebojsa Jojic , Vladimir Jojic , David E. Heckerman , Brendan John Frey , Christopher A. Meek
IPC分类号： G01N33/50
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.
摘要翻译：本发明提供了通过诸如成本函数，贪心算法，期望最大化（EM）算法等机器学习算法来促进艾滋病疫苗鸡尾酒组合的系统和方法。可以利用这种装配来产生疫苗鸡尾酒，在宿主免疫压力下快速发展的病原体。例如，本发明的系统和方法可以用于促进用于诸如HIV的病原体的T细胞疫苗的设计。此外，本发明的系统和方法可以与其他应用相结合使用，例如序列比对，基序发现，分类和重组热点检测。本文所述的新颖技术可以提供改进，以通过构建具有较高表位覆盖度的疫苗混合物来设计疫苗的传统方法，例如与来自数据的共同体，树节点和随机菌株的鸡尾酒相比。

2. 发明授权

US08181163B2 Program synthesis and debugging using machine learning techniques 有权
标题翻译：使用机器学习技术的程序综合和调试
公开(公告)号：US08181163B2
公开(公告)日：2012-05-15
申请号：US11745295
申请日：2007-05-07
申请人： Vladimir Jojic , Nebojsa Jojic , Sumit Gulwani
发明人： Vladimir Jojic , Nebojsa Jojic , Sumit Gulwani
IPC分类号： G06F9/45
CPC分类号： G06F11/3624 , G06F8/436
摘要： One embodiment is directed to synthesizing code fragments in a software routine using known inputs and corresponding expected outputs. A computer system provides a software routine with known inputs and corresponding expected outputs, infers software routine instructions based on the known inputs and corresponding expected outputs, and synthesizes a correctly functioning code fragment based on the inferred instructions. Another embodiment is directed to automatically resolving semantic errors in a software routine. A computer system provides the software routine with known inputs and corresponding expected outputs for portions of a program fragment where an error has been localized. The computer system learns a correctly functioning program fragment from pairs of input-output descriptions of the program fragment, determines the program statements that can transform given input states into given output states after execution of those program statements, and alters portions of the software routine with the learned program fragments.
摘要翻译：一个实施例涉及使用已知输入和相应的预期输出来合成软件例程中的代码片段。计算机系统提供具有已知输入和对应的预期输出的软件例程，基于已知输入和相应的预期输出推断软件程序指令，并且基于推断的指令来合成正确运行的代码片段。另一个实施例涉及自动解决软件程序中的语义错误。计算机系统向软件例程提供已知输入和对于已经本地化错误的程序片段的部分的相应的预期输出。计算机系统从程序片段的输入输出描述中学习正确运行的程序片段，确定在执行这些程序语句之后将给定的输入状态转换为给定的输出状态的程序语句，并且改变软件程序的一部分学习的程序片段。

3. 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。

4. 发明授权

US08396671B2 Cluster modeling, and learning cluster specific parameters of an adaptive double threading model 有权
标题翻译：自适应双线程模型的集群建模和学习集群特定参数
公开(公告)号：US08396671B2
公开(公告)日：2013-03-12
申请号：US11770684
申请日：2007-06-28
申请人： Nebojsa Jojic , David E. Heckerman , Manuel Jesus Reyes Gomez
发明人： Nebojsa Jojic , David E. Heckerman , Manuel Jesus Reyes Gomez
IPC分类号： G01N33/48
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。

5. 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。

6. 发明授权

US07647285B2 Tools for health and wellness 有权
标题翻译：健康和健康工具
公开(公告)号：US07647285B2
公开(公告)日：2010-01-12
申请号：US11556085
申请日：2006-11-02
申请人： David E. Heckerman , Craig J. Mundie , Nebojsa Jojic , Randy J. Hinrichs
发明人： David E. Heckerman , Craig J. Mundie , Nebojsa Jojic , Randy J. Hinrichs
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
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。

7. 发明申请

US20090171640A1 POPULATION SEQUENCING USING SHORT READ TECHNOLOGIES 审中-公开
标题翻译：使用短读技术的人口排序
公开(公告)号：US20090171640A1
公开(公告)日：2009-07-02
申请号：US11966446
申请日：2007-12-28
申请人： Nebojsa Jojic , Vladimir Jojic , Tomer Hertz
发明人： Nebojsa Jojic , Vladimir Jojic , Tomer Hertz
IPC分类号： G06G7/48
CPC分类号： G16B30/00 , G16B20/00
摘要： The claimed subject matter provides systems and/or methods that facilitate generating population sequences of strain variants included in a sample. Sequencing can be based on high throughput of short reads. Further, site variants exhibited in the short reads can be linked to reconstruct multiple full strains of a targeted gene, including low concentration variants in the sample. Cues in the short read data can be utilized to perform multi-strain assembly. For example, the cues can include different strain concentrations that lead to more frequently seen strains being responsible for more frequent reads and quilting of overlapping reads to infer mutation linkage over long stretches of DNA.
摘要翻译：所要求保护的主题提供了有助于生成包含在样品中的菌株变异种群序列的系统和/或方法。排序可以基于高读取的吞吐量。此外，在短读中显示的位点变体可以连接以重建靶基因的多个完整菌株，包括样品中的低浓度变体。短读数据中的提示可用于执行多应变组装。例如，线索可以包括不同的应变浓度，其导致更频繁地看到的菌株负责更频繁的读取和绗缝重叠读数以推断长延伸DNA上的突变连锁。

8. 发明申请

US20080172650A1 PROGRAM VERIFICATION AND DISCOVERY USING PROBABILISTIC INFERENCE 有权
标题翻译：使用概率论的程序验证和发现
公开(公告)号：US20080172650A1
公开(公告)日：2008-07-17
申请号：US11622904
申请日：2007-01-12
申请人： Sumit Gulwani , Vladimir Jojic , Nebojsa Jojic
发明人： Sumit Gulwani , Vladimir Jojic , Nebojsa Jojic
IPC分类号： G06F9/44
CPC分类号： G06F11/3608
摘要： In one embodiment, a computer system performs a method for verifying the validity or invalidity of a software routine by learning appropriate invariants at each program point. A computer system chooses an abstract domain that is sufficiently precise to express the appropriate invariants. The computer system associates an inconsistency measure with any two abstract elements of the abstract domain. The computer system searches for a set of local invariants configured to optimize a total inconsistency measure which includes a sum of local inconsistency measures. The computer system optimizes the total inconsistency measure for all input/output pairs of the software routine. In one embodiment, the optimization of total inconsistency is achieved by the computer system which repeatedly replaces a locally inconsistent invariant with a new invariant, randomly selected among the possible invariants which are locally less inconsistent with the current invariants at the neighboring program points.
摘要翻译：在一个实施例中，计算机系统通过在每个程序点学习适当的不变量来执行用于验证软件例程的有效性或无效性的方法。计算机系统选择足够精确的表示适当不变量的抽象域。计算机系统将不一致性度量与抽象域的任意两个抽象元素相关联。计算机系统搜索一组局部不变量，其被配置为优化包括本地不一致性度量的总和的总不一致性度量。计算机系统优化软件程序的所有输入/输出对的总不一致性测量。在一个实施例中，总体不一致性的优化是通过计算机系统实现的，该计算机系统在局部地与邻近程序点的当前不变量局部较不一致的可能不变量中随机选择新的不变量来重复地替换局部不一致的不变量。

9. 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。

10. 发明申请

US20060095241A1 Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails 审中-公开
标题翻译：利用机器学习算法方便装配疫苗鸡尾酒的系统和方法
公开(公告)号：US20060095241A1
公开(公告)日：2006-05-04
申请号：US10977415
申请日：2004-10-29
申请人： Nebojsa Jojic , Vladimir Jojic
发明人： Nebojsa Jojic , Vladimir Jojic
IPC分类号： G06G7/48 , G06G7/58
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 which optimize an optimization criterion, via machine learning algorithms, e.g., 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.
摘要翻译：本发明提供了通过机器学习算法（例如，贪心算法，期望最大化（EM））算法等来促进艾滋病疫苗混合物组合的优化优化标准的系统和方法。可以利用这种装配来产生疫苗鸡尾酒对于在宿主的免疫压力下迅速发展的病原体物种。例如，本发明的系统和方法可以用于促进用于诸如HIV的病原体的T细胞疫苗的设计。此外，本发明的系统和方法可以与其他应用相结合使用，例如序列比对，基序发现，分类和重组热点检测。本文所述的新颖技术可以提供改进，以通过构建具有较高表位覆盖度的疫苗混合物来设计疫苗的传统方法，例如与来自数据的共同体，树节点和随机菌株的鸡尾酒相比。

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