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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。
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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
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。
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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。
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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
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。
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5.发明申请Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails 审中-公开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
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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6.发明授权公开(公告)号：US08478535B2
公开(公告)日：2013-07-02
申请号：US11324506
申请日：2005-12-30
IPC分类号： G01N33/50
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。
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公开(公告)号：US07729999B2
公开(公告)日：2010-06-01
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： G06F15/18
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。
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公开(公告)号：US20080282108A1
公开(公告)日：2008-11-13
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
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。
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9.发明申请公开(公告)号：US20060190226A1
公开(公告)日：2006-08-24
申请号：US11324691
申请日：2005-12-30
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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公开(公告)号：US20060178861A1
公开(公告)日：2006-08-10
申请号：US11324506
申请日：2005-12-30
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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