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公开(公告)号：US20150261926A1
公开(公告)日：2015-09-17
申请号：US14673697
申请日：2015-03-30
CPC分类号： G06N99/005 , G06F19/18 , G06F19/24 , G06Q10/04
摘要： A method, computer system, and computer memory medium optimizing a transductive model Mx suitable for use in data analysis and for determining a prognostic outcome specific to a particular subject are disclosed. The particular subject may be represented by an input vector, which includes a number of variable features in relation to a scenario of interest. Samples from a global dataset D also having the same features relating to the scenario and for which the outcome is known are determined. In an embodiment, a subset of the variable features within a neighborhood formed by the samples are ranked in order of importance to an outcome. The prognostic transductive model is then created based, at least in part, on the subset, the ranking, and the neighborhood. The subset and the neighborhood are then optimized until the accuracy of the transductive model is maximized.
摘要翻译： 公开了一种优化适用于数据分析和确定特定对象特定的预后结果的转换模型Mx的方法，计算机系统和计算机存储介质。 特定主体可以由输入向量表示，输入向量包括与感兴趣的场景相关的多个可变特征。 来自全球数据集D的样本也具有与场景相关的功能，并且结果已知的样本被确定。 在一个实施例中，由样本形成的邻域中的可变特征的子集按照对结果的重要性的顺序排列。 然后，至少部分地基于子集，排名和邻域创建预后转换模型。 然后优化子集和邻域，直到转换模型的精度最大化。
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2.发明授权Medical applications of adaptive learning systems using gene expression data 有权使用基因表达数据的自适应学习系统的医学应用公开(公告)号：US07370021B2
公开(公告)日：2008-05-06
申请号：US10507737
申请日：2003-03-17
申请人： Anthony Edmund Reeve , Mathias Erwin Futschik , Michael James Sullivan , Nikola Kirilov Kasabov , Parry John Guilford
发明人： Anthony Edmund Reeve , Mathias Erwin Futschik , Michael James Sullivan , Nikola Kirilov Kasabov , Parry John Guilford
CPC分类号： G06F19/20 , G06N3/004 , G06N3/0427 , G06N3/0436
摘要： A neural network module is provided. It comprises an input layer comprising one or more input nodes configured to receive gene expression data. It also has a rule base layer comprising one or more rule nodes and an output layer comprising one or more output nodes configured to output one or more conditions. It also comprises an adaptive component configured to extract one or more rules from the rule base layer representing relationships between the gene expression data and the one or more conditions. Methods and systems using the module are disclosed as well as specific profiles utilising the system.
摘要翻译： 提供神经网络模块。 其包括输入层，其包括被配置为接收基因表达数据的一个或多个输入节点。 它还具有包括一个或多个规则节点的规则基础层和包括被配置为输出一个或多个条件的一个或多个输出节点的输出层。 它还包括被配置为从表示基因表达数据与一个或多个条件之间的关系的规则基层提取一个或多个规则的自适应组件。 公开了使用该模块的方法和系统以及利用该系统的特定简档。
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公开(公告)号：US07089217B2
公开(公告)日：2006-08-08
申请号：US10257214
申请日：2001-04-10
CPC分类号： G06N3/0436
摘要： A neural network module including an input layer having one or more input nodes arranged to receive input data, a rule base layer having one or more rule nodes, an output layer having one or more output nodes, and an adaptive component arranged to aggregate selected two or more rule nodes in the rule base layer based on the input data, an adaptive learning system having one or more of the neural network modules, related methods of implementing the neural network module and an adaptive learning system, and a neural network program.
摘要翻译： 一种神经网络模块，包括具有布置成接收输入数据的一个或多个输入节点的输入层，具有一个或多个规则节点的规则基础层，具有一个或多个输出节点的输出层，以及布置成聚集所选择的两个 基于输入数据的规则库中的规则节点，具有一个或多个神经网络模块的自适应学习系统，实现神经网络模块的相关方法和自适应学习系统以及神经网络程序。
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公开(公告)号：US09195949B2
公开(公告)日：2015-11-24
申请号：US14673697
申请日：2015-03-30
CPC分类号： G06N99/005 , G06F19/18 , G06F19/24 , G06Q10/04
摘要： A method, computer system, and computer memory medium optimizing a transductive model Mx suitable for use in data analysis and for determining a prognostic outcome specific to a particular subject are disclosed. The particular subject may be represented by an input vector, which includes a number of variable features in relation to a scenario of interest. Samples from a global dataset D also having the same features relating to the scenario and for which the outcome is known are determined. In an embodiment, a subset of the variable features within a neighborhood formed by the samples are ranked in order of importance to an outcome. The prognostic transductive model is then created based, at least in part, on the subset, the ranking, and the neighborhood. The subset and the neighborhood are then optimized until the accuracy of the transductive model is maximized.
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公开(公告)号：US09002682B2
公开(公告)日：2015-04-07
申请号：US13088306
申请日：2011-04-15
CPC分类号： G06N99/005 , G06F19/18 , G06F19/24 , G06Q10/04
摘要： A method, computer system, and computer memory medium optimizing a transductive model Mx suitable for use in data analysis and for determining a prognostic outcome specific to a particular subject are disclosed. The particular subject may be represented by an input vector, which includes a number of variable features in relation to a scenario of interest. Samples from a global dataset D also having the same features relating to the scenario and for which the outcome is known are determined. In an embodiment, a subset of the variable features within a neighborhood formed by the samples are ranked in order of importance to an outcome. The prognostic transductive model is then created based, at least in part, on the subset, the ranking, and the neighborhood. The subset and the neighborhood are then optimized until the accuracy of the transductive model is maximized.
摘要翻译： 公开了一种优化适用于数据分析和确定特定对象特定的预后结果的转换模型Mx的方法，计算机系统和计算机存储介质。 特定主体可以由输入向量表示，输入向量包括与感兴趣的场景相关的多个可变特征。 来自全球数据集D的样本也具有与场景相关的功能，并且结果已知的样本被确定。 在一个实施例中，由样本形成的邻域中的可变特征的子集按照对结果的重要性的顺序排列。 然后，至少部分地基于子集，排名和邻域创建预后转换模型。 然后优化子集和邻域，直到转换模型的精度最大化。
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公开(公告)号：US20110307228A1
公开(公告)日：2011-12-15
申请号：US13088306
申请日：2011-04-15
IPC分类号： G06F17/16
CPC分类号： G06N99/005 , G06F19/18 , G06F19/24 , G06Q10/04
摘要： A method of optimising a model Mx suitable for use in data analysis and determining a prognostic outcome specific to a particular subject (input vector x), the subject comprising a number of variable features in relation to a scenario of interest for which there is a global dataset D of samples also having the same features relating to the scenario, and for which the outcome is known is disclosed. In one implementation, the method includes: (a) determining what number and a subset Vx of variable features will be used in assessing the outcome for the input vector x; (b) determining what number Kx of samples from within the global data set D will form a neighbourhood about x; (c) selecting suitable Kx samples from the global data set which have the variable features that most closely accord to the variable features of the particular subject x to form the neighbourhood Dx; (d) ranking the Vx variable features within the neighbourhood Dx in order of importance to the outcome of vector x and obtaining a weight vector Wx for all variable features Vx; (e) creating a prognostic model Mx, having a set of model parameters Px and the other parameters from (a)-(d); (f) testing the accuracy of the model Mx at e) for each sample from Dx; (g) storing both the accuracy from (f), and the model parameters developed in (a) to (e); (h) repeating (a) and/or (b) whilst applying an optimisation procedure to optimise Vx and/or Kx, to determine their optimal values, before repeating (c)-(h) until maximum accuracy at (f) is achieved.
摘要翻译： 一种优化适用于数据分析和确定特定对象（输入向量x）特定的预后结果的模型Mx的方法，所述对象包括与感兴趣的场景相关的多个可变特征，其中存在全局 具有与场景相关的特征的样本的数据集D也被公开。 在一个实现中，该方法包括：（a）确定在评估输入向量x的结果时将使用可变特征的数量和子集Vx; （b）确定来自全球数据集合D内的样本的数量Kx将形成关于x的邻域; （c）从全局数据集中选择合适的Kx样本，该样本具有与特定受试者x的可变特征最相符的可变特征以形成邻域Dx; （d）按照对向量x的结果的重要性的顺序对邻域Dx内的Vx变量特征进行排名，并获得所有可变特征Vx的权重向量Wx; （e）创建具有一组模型参数Px和（a） - （d）的其他参数的预测模型Mx; （f）对于来自Dx的每个样品，e）测试模型Mx的精度; （g）存储（f）和（a）至（e）中制定的模型参数的精度; （h）在重复（c） - （h）之前，重复（a）和/或（b）同时应用优化程序来优化Vx和/或Kx以确定其最佳值，直到达到（f）的最大精度 。
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