会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 发明申请
    • Method and a system for extracting a genotype-phenotype relationship
    • 方法和提取基因型 - 表型关系的系统
    • US20080138799A1
    • 2008-06-12
    • US11247218
    • 2005-10-12
    • Jie ChengMathaeus DejoriMartin StetterBernd Wachmann
    • Jie ChengMathaeus DejoriMartin StetterBernd Wachmann
    • C12Q1/68G06F19/00
    • G16B40/00G16B20/00
    • At least one genotype-phenotype relationship is extracted based on genotype data of a group of genes for different organisms of a group of organisms. A first database stores genotype data of each organism of the group of organisms. For each organism a genotype vector is stored having a vector component for each gene of the group of genes. A second database stores phenotype data of each organism of the group of organisms. For each organism a phenotype vector is stored having a vector component for each phenotype feature of a group of phenotype features of the organism. A calculation unit uses a machine learning process to classify organisms with different phenotypes depending on the genotype vectors stored in the first database and the phenotype vectors stored in the second database to extract the genotype-phenotype relationship.
    • 基于一组生物体的不同生物体的一组基因的基因型数据提取至少一种基因型 - 表型关系。 第一数据库存储该组生物体的每个生物体的基因型数据。 对于每个生物体,存储基因型载体,其具有基因组中每个基因的载体成分。 第二个数据库存储生物群组中每个生物体的表型数据。 对于每个生物体,存储表型载体,其具有用于生物体的一组表型特征的每个表型特征的载体成分。 计算单元使用机器学习过程根据存储在第一数据库中的基因型载体和存储在第二数据库中的表型载体来分类具有不同表型的生物体以提取基因型 - 表型关系。
    • 4. 发明申请
    • Machine learning with robust estimation, bayesian classification and model stacking
    • 机器学习与鲁棒估计,贝叶斯分类和模型堆叠
    • US20060059112A1
    • 2006-03-16
    • US11208988
    • 2005-08-22
    • Jie ChengBernd WachmannClaus Neubauer
    • Jie ChengBernd WachmannClaus Neubauer
    • G06F15/18
    • G06N7/005G06K9/623G06K9/6296
    • A system and method for machine learning are provided, the system including a processor, an adapter for receiving instances for two different classes where each instance has a vector of feature values, a filtering unit for estimating distances between two corresponding instances of the two different classes for each of a plurality of estimators, a selection unit for calculating a corresponding p-value for each distance where the p-value is the statistical significance that the two feature vectors of the corresponding instances have different origins, and an evaluation unit for combining the different estimators by choosing the highest calculated p-value; and the method including receiving instances for two different classes, each instance having a vector of feature values, estimating distances between two corresponding instances of the two different classes for each of several of estimators, calculating a corresponding p-value for each distance, where the p-value is the statistical significance that the two feature vectors of the corresponding instances have different origins, and combining the different estimators by choosing the highest calculated p-value.
    • 提供了一种用于机器学习的系统和方法,所述系统包括处理器,用于接收两个不同类别的实例的适配器,每个实例具有特征值向量,滤波单元,用于估计两个不同类别的两个对应实例之间的距离 对于多个估计器中的每一个,选择单元,用于计算每个距离的相应p值,其中p值是相应实例的两个特征向量具有不同来源的统计重要性;以及评估单元, 通过选择最高计算的p值来估计不同的估计值; 并且所述方法包括接收两个不同类别的实例,每个实例具有特征值向量,估计几个估计器中的每一个的两个不同类别的两个对应实例之间的距离,计算每个距离的相应p值, p值是相应实例的两个特征向量具有不同来源的统计意义,并且通过选择最高计算的p值来组合不同的估计器。
    • 6. 发明申请
    • Method and Apparatus for Classifying Tissue Using Image Data
    • 使用图像数据分类组织的方法和装置
    • US20070123773A1
    • 2007-05-31
    • US11426329
    • 2006-06-26
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • A61B5/05
    • G06T7/0012G06K9/00147G06K9/6296G06T2207/30024
    • Disclosed is a technique for classifying tissue based on image data. A plurality of tissue parameters are extracted from image data (e.g., magnetic resonance image data) to be classified. The parameters are preprocessed, and the tissue is classified using a classification algorithm and the preprocessed parameters. In one embodiment, the parameters are preprocessed by discretization of the parameters. The classification algorithm may use a decision model for the classification of the tissue, and the decision model may be generated by performing a machine learning algorithm using preprocessed tissue parameters in a training set of data. In one embodiment, the machine learning algorithm generates a Bayesian network. The image data used may be magnetic resonance image data that was obtained before and after the intravenous administration of lymphotropic superparamagnetic nanoparticles.
    • 公开了一种基于图像数据对组织进行分类的技术。 从要分类的图像数据(例如磁共振图像数据)中提取多个组织参数。 对参数进行预处理,使用分类算法和预处理参数对组织进行分类。 在一个实施例中,参数通过离散化参数进行预处理。 分类算法可以使用用于组织分类的决策模型,并且可以通过在训练数据集中执行使用预处理的组织参数的机器学习算法来生成决策模型。 在一个实施例中,机器学习算法生成贝叶斯网络。 使用的图像数据可以是在静脉内施用嗜热超顺磁性纳米颗粒之前和之后获得的磁共振图像数据。
    • 7. 发明授权
    • Method and apparatus for classifying tissue using image data
    • 使用图像数据分类组织的方法和装置
    • US07720267B2
    • 2010-05-18
    • US11426329
    • 2006-06-26
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • Thomas FuchsBernd WachmannClaus NeubauerJie Cheng
    • G06K9/00A61B5/05
    • G06T7/0012G06K9/00147G06K9/6296G06T2207/30024
    • Disclosed is a technique for classifying tissue based on image data. A plurality of tissue parameters are extracted from image data (e.g., magnetic resonance image data) to be classified. The parameters are preprocessed, and the tissue is classified using a classification algorithm and the preprocessed parameters. In one embodiment, the parameters are preprocessed by discretization of the parameters. The classification algorithm may use a decision model for the classification of the tissue, and the decision model may be generated by performing a machine learning algorithm using preprocessed tissue parameters in a training set of data. In one embodiment, the machine learning algorithm generates a Bayesian network. The image data used may be magnetic resonance image data that was obtained before and after the intravenous administration of lymphotropic superparamagnetic nanoparticles.
    • 公开了一种基于图像数据对组织进行分类的技术。 从要分类的图像数据(例如磁共振图像数据)中提取多个组织参数。 对参数进行预处理,使用分类算法和预处理参数对组织进行分类。 在一个实施例中,参数通过离散化参数进行预处理。 分类算法可以使用用于组织分类的决策模型,并且可以通过在训练数据集中执行使用预处理的组织参数的机器学习算法来生成决策模型。 在一个实施例中,机器学习算法生成贝叶斯网络。 使用的图像数据可以是在静脉内施用嗜热超顺磁性纳米颗粒之前和之后获得的磁共振图像数据。
    • 9. 发明授权
    • Plug connector with improved cable arrangement
    • 插头连接器具有改进的电缆布置
    • US07585184B2
    • 2009-09-08
    • US12012620
    • 2008-02-04
    • Ping-Sheng SuJie ChengHong-Lei Fan
    • Ping-Sheng SuJie ChengHong-Lei Fan
    • H01R9/05
    • H01R27/02H01R13/567
    • A plug connector includes an insulative housing (1) including a base portion (10) and a mating portion (12) extending from the base portion in a first direction, a number of contacts (2) received in the insulative housing, and a number of wires (3) including a number of signal wires (31) and a number of power wires (32) respectively electrically connecting with the contacts. The signal wires and the power wires are both arranged to extend along a second direction perpendicular to the first direction of the mating portion. The power wires are arranged into two groups respectively symmetrically arranged relative to the base portion along the second direction and electrically connect to the same contacts.
    • 插头连接器包括绝缘壳体(1),其包括基部(10)和从第一方向从基部延伸的配合部分(12),接纳在绝缘壳体中的多个触点(2) 包括分别与触点电连接的多个信号线(31)和多个电源线(32)的电线(3)。 信号线和电源线均布置成沿着垂直于配合部分的第一方向的第二方向延伸。 电源线被布置成沿着第二方向相对于基部对称布置的两组,并且电连接到相同的触点。
    • 10. 发明申请
    • Plug connector with improved cable arrangement
    • 插头连接器具有改进的电缆布置
    • US20080188136A1
    • 2008-08-07
    • US12012620
    • 2008-02-04
    • Ping-Sheng SuJie ChengHong-Lei Fan
    • Ping-Sheng SuJie ChengHong-Lei Fan
    • H01R24/00
    • H01R27/02H01R13/567
    • A plug connector includes an insulative housing (1) including a base portion (10) and a mating portion (12) extending from the base portion in a first direction, a number of contacts (2) received in the insulative housing, and a number of wires (3) including a number of signal wires (31) and a number of power wires (32) respectively electrically connecting with the contacts. The signal wires and the power wires are both arranged to extend along a second direction perpendicular to the first direction of the mating portion. The power wires are arranged into two groups respectively symmetrically arranged relative to the base portion along the second direction and electrically connect to the same contacts.
    • 插头连接器包括绝缘壳体(1),其包括基部(10)和从第一方向从基部延伸的配合部分(12),接纳在绝缘壳体中的多个触点(2) 包括分别与触点电连接的多个信号线(31)和多个电源线(32)的电线(3)。 信号线和电源线均布置成沿着垂直于配合部分的第一方向的第二方向延伸。 电源线被布置成沿着第二方向相对于基部对称布置的两组,并且电连接到相同的触点。