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1. 发明授权

US09002682B2 Data analysis and predictive systems and related methodologies 有权
标题翻译：数据分析和预测系统及相关方法
公开(公告)号：US09002682B2
公开(公告)日：2015-04-07
申请号：US13088306
申请日：2011-04-15
申请人： Nikola Kirilov Kasabov
发明人： Nikola Kirilov Kasabov
IPC分类号： G06F17/10 , G06F7/60 , G06N99/00 , G06F19/24 , G06Q10/04 , G06F19/18
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。

2. 发明申请

US20110307228A1 DATA ANALYSIS AND PREDICTIVE SYSTEMS AND RELATED METHODOLOGIES 有权
标题翻译：数据分析与预测系统及相关方法
公开(公告)号：US20110307228A1
公开(公告)日：2011-12-15
申请号：US13088306
申请日：2011-04-15
申请人： Nikola Kirilov Kasabov
发明人： Nikola Kirilov Kasabov
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。

3. 发明授权

US07370021B2 Medical applications of adaptive learning systems using gene expression data 有权
标题翻译：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
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
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。

4. 发明授权

US07089217B2 Adaptive learning system and method 失效
标题翻译：自适应学习系统和方法
公开(公告)号：US07089217B2
公开(公告)日：2006-08-08
申请号：US10257214
申请日：2001-04-10
申请人： Nikola Kirilov Kasabov
发明人： Nikola Kirilov Kasabov
IPC分类号： G06F15/18 , G06F17/00
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。

5. 发明申请

US20150261926A1 DATA ANALYSIS AND PREDICTIVE SYSTEMS AND RELATED METHODOLOGIES 审中-公开
标题翻译：数据分析与预测系统及相关方法
公开(公告)号：US20150261926A1
公开(公告)日：2015-09-17
申请号：US14673697
申请日：2015-03-30
申请人： Nikola Kirilov Kasabov
发明人： Nikola Kirilov Kasabov
IPC分类号： G06F19/00 , G06N99/00
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。

6. 发明申请

US20050256815A1 Medical applications of adaptive learning systems using gene expression data 有权
标题翻译：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：US20050256815A1
公开(公告)日：2005-11-17
申请号：US10507737
申请日：2003-03-17
申请人： Anthony Reeve , Mathias Futschik , Michael Sullivan , Nikola Kasabov , Parry Guilford
发明人： Anthony Reeve , Mathias Futschik , Michael Sullivan , Nikola Kasabov , Parry Guilford
IPC分类号： G01N33/53 , C12N15/00 , C12Q1/68 , G01N33/574 , G06F19/00 , G06N3/00 , G06N3/04 , G06F15/18 , G01N33/48 , G01N33/50
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。

7. 发明授权

US09195949B2 Data analysis and predictive systems and related methodologies 有权
公开(公告)号：US09195949B2
公开(公告)日：2015-11-24
申请号：US14673697
申请日：2015-03-30
申请人： Nikola Kirilov Kasabov
发明人： Nikola Kirilov Kasabov
IPC分类号： G06F17/10 , G06F7/60 , G06N99/00 , G06F19/24
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.

8. 发明申请

US20060129034A1 Medical decision support systems utilizing gene expression and clinical information and method for use 审中-公开
标题翻译：利用基因表达和临床信息和使用方法的医疗决策支持系统
公开(公告)号：US20060129034A1
公开(公告)日：2006-06-15
申请号：US10524754
申请日：2003-08-15
申请人： Nikola Kasabov , Matthias Futschik , Michael Sullivan , Anthony Reeve
发明人： Nikola Kasabov , Matthias Futschik , Michael Sullivan , Anthony Reeve
IPC分类号： A61B5/00
CPC分类号： G16H50/20 , G16B20/00 , G16B25/00 , G16B40/00 , G16H50/70
摘要： Embodiments of this invention provide improved medical decision support systems and methods for using such systems to simultaneously consider two or more different types of information along with estimates of accuracies of the information to produce a combined predictor. Such predictors have greater accuracy compared to use of the individual types of information alone. Increased accuracy can increase the likelihood of correct diagnosis and/or evaluation of clinical condition or outcome, and can decrease the frequency of false negative results, including misdiagnosis. Embodiments of medical decision support systems can include EFuNN, Bayesian or other statistical estimators to produce a combined predictor. The systems can be used to extract relationship rules between sets of genes and clinical variables common for patients of a group, thus making a personalized gene-based treatment possible. Such systems are incorporated into computer-based devices and are run using suitable computer programs. Outputs can be directed to hard-copy devices for printing, or can be transmitted remotely to a terminal at a location where a practitioner is interacting with a patient.
摘要翻译：本发明的实施例提供改进的医疗决策支持系统和方法，用于使用这样的系统来同时考虑两种或更多种不同类型的信息以及信息的准确性的估计以产生组合预测器。与单独使用各种类型的信息相比，这种预测器具有更高的准确度。提高准确度可以增加正确诊断和/或评估临床状况或结果的可能性，并可以减少假阴性结果的频率，包括误诊。医疗决策支持系统的实施例可以包括EFuNN，贝叶斯或其他统计估计器以产生组合预测器。该系统可用于提取基因组之间的关系规则和一组患者常见的临床变量，从而进行个性化的基于基因的治疗。这样的系统被并入基于计算机的设备中，并且使用合适的计算机程序来运行。输出可以被引导到用于打印的硬拷贝设备，或者可以在从业者与患者交互的位置远程传输到终端。

9. 发明申请

WO2005038774A1 ADAPTIVE SOUND AND IMAGE LEARNING SYSTEM AND METHOD 审中-公开
标题翻译：自适应声音和图像学习系统和方法
公开(公告)号：WO2005038774A1
公开(公告)日：2005-04-28
申请号：PCT/NZ2004/000264
申请日：2004-10-22
申请人： KASABOV, Nikola, Kirilov , GHOBAKHLOU, Ali, Akbar
发明人： KASABOV, Nikola, Kirilov , GHOBAKHLOU, Ali, Akbar
IPC分类号： G10L15/00
CPC分类号： G10L15/16 , G10L15/18
摘要： The invention provides an adaptive sound and/or image recognition system. The system includes an input layer comprising one or more input nodes configured to receive input data. A rule base layer comprising one or more rule nodes associated with respective functions is configured to receive input from the input layer. An output layer comprising one or more output nodes associated with respective functions is configured to receive input from the rule base layer. The invention further provides methods of recognising an input sound and/or image signal.
摘要翻译：本发明提供一种自适应声音和/或图像识别系统。该系统包括输入层，该输入层包括被配置为接收输入数据的一个或多个输入节点。包括与相应功能相关联的一个或多个规则节点的规则基础层被配置为从输入层接收输入。包括与相应功能相关联的一个或多个输出节点的输出层被配置为从规则基础层接收输入。本发明还提供了识别输入声音和/或图像信号的方法。

10. 发明申请

WO2004016218A3 MEDICAL DECISION SUPPORT SYSTEMS UTILIZING GENE EXPRESSION AND CLINICAL INFORMATION AND METHOD FOR USE 审中-公开
公开(公告)号：WO2004016218A3
公开(公告)日：2004-02-26
申请号：PCT/US2003/025563
申请日：2003-08-15
申请人： PACIFIC EDGE BIOTECHNOLOGY, LTD. , FARMER, Charles, Davis. Jr. , KASABOV, Nikola, Kirilov , FUTSCHIK, Matthias, Erwin , SULLIVAN, Michael, James , REEVE, Anthony, Edmund
发明人： KASABOV, Nikola, Kirilov , FUTSCHIK, Matthias, Erwin , SULLIVAN, Michael, James , REEVE, Anthony, Edmund
IPC分类号： A61B5/00
摘要： Embodiments of this invention provide improved medical decision support systems and methods for using such systems to simultaneously consider two or more different types of information along with estimates of accuracies of the information to produce a combined predictor (Figure 1). Such predictors have greater accuracy compared to use of the individual types of information alone. Increased accuracy can increase the likelihood of correct diagnosis and/or evaluation of clinical condition or outcome, and can decrease the frequency of false negative results, including misdiagnosis. Embodiments of medical decision support systems can include EFuNN, Bayesian or other statistical estimators to produce a combined predictor. The systems can be used to extract relationship rules between sets of genes and clinical variables common for patients of a group, thus making a personalized gene-based treatment possible. Such systems are incorporated into computer-based devices and are run using suitable computer programs.

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