公开(公告)号：US20050260671A1

公开(公告)日：2005-11-24

申请号：US11189808

申请日：2005-07-27

申请人： Ben Hitt ,  Peter Levine ,  Emanuel Petricoin ,  Lance Liotta

发明人： Ben Hitt ,  Peter Levine ,  Emanuel Petricoin ,  Lance Liotta

IPC分类号： G01N27/62 ,  G01N33/48 ,  G06F19/00 ,  C12Q1/68 ,  G01N33/50

CPC分类号： G16H10/40 ,  G16B25/00 ,  G16B40/00 ,  G16H50/70 ,  Y10S707/99933 ,  Y10S707/99943

摘要： The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.

公开(公告)号：US20080103063A1

公开(公告)日：2008-05-01

申请号：US11968262

申请日：2008-01-02

申请人： Ben Hitt ,  Peter Levine ,  Emanuel Petricoin

发明人： Ben Hitt ,  Peter Levine ,  Emanuel Petricoin

IPC分类号： C40B50/02

CPC分类号： G01N33/6848 ,  C40B30/04 ,  G01N33/6803 ,  G16B25/00 ,  G16B40/00 ,  Y10T436/24

摘要： The present invention relates to a method of quality assurance/quality control for high-throughput bioassay processes. The method includes generating a bioassay process model, and then comparing spectral data based on a combination of a biochip and a test serum to the bioassay process model to determine if the test sample and the bioassay process are producing acceptable data. Alternatively, the method may include comparing spectral data based on a combination of serum and diluents used in an electrospray process to the bioassay process model. If the bioassay process and test sample fall within the model, then the spectrum produced may be further analyzed.

摘要翻译： 本发明涉及高通量生物测定方法的质量保证/质量控制方法。 该方法包括生成生物测定过程模型，然后将基于生物芯片和测试血清的组合的光谱数据与生物测定过程模型进行比较，以确定测试样品和生物测定过程是否产生可接受的数据。 或者，该方法可以包括将基于电喷雾过程中使用的血清和稀释剂的组合的光谱数据与生物测定过程模型进行比较。 如果生物测定过程和测试样品落在模型中，则可以进一步分析产生的光谱。

公开(公告)号：US20050209786A1

公开(公告)日：2005-09-22

申请号：US11008784

申请日：2004-12-10

申请人： Tzong-Hao Chen ,  Ben Hitt ,  Peter Levine

发明人： Tzong-Hao Chen ,  Ben Hitt ,  Peter Levine

IPC分类号： G01N33/48 ,  G01N33/50 ,  G06F19/24 ,  G06F19/28 ,  G06F19/00

CPC分类号： G06K9/6221 ,  G06F19/24 ,  G06F19/28 ,  G06K9/6262 ,  G16H50/50 ,  Y10T436/24

摘要： A model of a particular biological state can be developed. The model may be used to determine if an unknown biological sample exhibits a particular biological state. This can be done by receiving either a biological sample or data associated with the biological sample. After the data is received, the data may be input into the model. In one embodiment, the acquisition of the data associated with the biological sample is performed at a first location and the imputing of the data into the model is performed at a second location different than the first location. Unless the data maps identically to the model, the data would have an inherent effect on the position of the particular clusters within the discriminatory pattern, if it is allowed to affect the model. The modeling software can keep track of the net effect on the model that each sample received has on the position of the model. If the model has drifted outside of a predetermined tolerance, the model can be updated. Various business relationships may be developed to undertake various steps of the overall method for providing a diagnosis to a patient.

摘要翻译： 可以开发出特定生物状态的模型。 该模型可用于确定未知的生物样品是否表现出特定的生物学状态。 这可以通过接收生物样品或与生物样品相关的数据来完成。 在接收到数据之后，可以将数据输入到模型中。 在一个实施例中，在第一位置处执行与生物样本相关联的数据的获取，并且在与第一位置不同的第二位置处执行将数据推算到模型中。 除非数据与模型相同，如果允许影响模型，数据将会对特定群集在歧视模式中的位置产生固有的影响。 建模软件可以跟踪每个样本收到的模型对模型的净效应。 如果模型已经偏移到预定的公差之外，则可以更新模型。 可以开发各种业务关系以承担向患者提供诊断的整体方法的各种步骤。

公开(公告)号：US20050043593A9

公开(公告)日：2005-02-24

申请号：US09906661

申请日：2001-07-18

申请人： Ben Hitt ,  Emanuel Petricoin III ,  Peter Levine ,  Lance Liotta

发明人： Ben Hitt ,  Emanuel Petricoin III ,  Peter Levine ,  Lance Liotta

IPC分类号： G01N27/62 ,  G01N33/48 ,  G06F19/00 ,  G01N33/50 ,  C12Q1/68 ,  A61B5/00

CPC分类号： G06F19/24 ,  G06F19/00 ,  G06F19/20 ,  G16H10/40 ,  G16H50/70 ,  Y10S707/99933 ,  Y10S707/99943

摘要： The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.

公开(公告)号：US20060064253A1

公开(公告)日：2006-03-23

申请号：US11093018

申请日：2005-03-30

申请人： Ben Hitt ,  Peter Levine

发明人： Ben Hitt ,  Peter Levine

IPC分类号： G06F19/00

CPC分类号： G16B20/00 ,  G16B40/00

摘要： A well-controlled serum study set (n=248) from women being followed and evaluated for the presence of ovarian cancer was used to extend serum proteomic pattern analysis to a higher resolution mass spectrometer instrument platform to explore the existence of multiple distinct highly accurate diagnostic sets of features present in the same mass spectrum. Multiple highly accurate diagnostic proteomic feature sets exist within human sera mass spectra. Using high-resolution mass spectral data, at least 56 different patterns were discovered that achieve greater than 85% sensitivity and specificity in testing and validation. Four of those feature sets exhibited 100% sensitivity and specificity in blinded validation. The sensitivity and specificity of diagnostic models generated from high-resolution mass spectral data were superior (P

摘要翻译： 使用来自妇女的良好控制的血清研究组（n = 248）并评估卵巢癌的存在，将血清蛋白质组学模式分析扩展到更高分辨率的质谱仪仪器平台，以探索存在多种不同的高精度诊断 在同一质谱中存在的特征集合。 人血清质谱中存在多个高度准确的诊断蛋白质组特征集。 使用高分辨率质谱数据，发现了至少56种不同的模式，其在测试和验证中实现了大于85％的灵敏度和特异性。 其中四个特征集在盲法验证中表现出100％的灵敏度和特异性。 从高分辨率质谱数据生成的诊断模型的灵敏度和特异性优于使用相同输入样本的低分辨率质谱数据产生的诊断模型的灵敏度和特异性（P <0.00001）。

公开(公告)号：US20060112041A1

公开(公告)日：2006-05-25

申请号：US11273432

申请日：2005-11-15

申请人： Ben Hitt

发明人： Ben Hitt

IPC分类号： G06F15/18

CPC分类号： G06K9/6217 ,  G06K9/6228 ,  G06K9/6284 ,  G06N3/08 ,  Y10S706/90 ,  Y10S706/932

摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

公开(公告)号：US20070185824A1

公开(公告)日：2007-08-09

申请号：US11735028

申请日：2007-04-13

申请人： Ben Hitt

发明人： Ben Hitt

IPC分类号： G06N3/08

CPC分类号： G06K9/6217 ,  G06K9/6228 ,  G06K9/6284 ,  G06N3/08 ,  Y10S706/90 ,  Y10S706/932

摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

摘要翻译： 本发明涉及用于对象分类的启发式算法。 第一学习算法包括用于抽取与每个对象相关联的数据流的遗传算法和用于对对象进行分类并测量遗传算法染色体的适应度的模式识别算法。 学习算法应用于训练数据集。 学习算法生成一个分类算法，用于对未知对象进行分类或分类。 本发明对文本和医疗样本进行分类，根据其他金融市场价格变动预测一个金融市场的行为，监测复杂流程设施状况以检测即将发生的失败。

公开(公告)号：US07499891B2

公开(公告)日：2009-03-03

申请号：US11735028

申请日：2007-04-13

申请人： Ben Hitt

发明人： Ben Hitt

IPC分类号： G06F15/18

CPC分类号： G06K9/6217 ,  G06K9/6228 ,  G06K9/6284 ,  G06N3/08 ,  Y10S706/90 ,  Y10S706/932

摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

公开(公告)号：US07240038B2

公开(公告)日：2007-07-03

申请号：US11273432

申请日：2005-11-15

申请人： Ben Hitt

发明人： Ben Hitt

IPC分类号： G06F15/18

CPC分类号： G06K9/6217 ,  G06K9/6228 ,  G06K9/6284 ,  G06N3/08 ,  Y10S706/90 ,  Y10S706/932

摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

公开(公告)号：US20070003996A1

公开(公告)日：2007-01-04

申请号：US11350269

申请日：2006-02-09

申请人： Ben Hitt ,  Brian Mansfield ,  Ping Yip

发明人： Ben Hitt ,  Brian Mansfield ,  Ping Yip

IPC分类号： G06F19/00 ,  C12Q1/04

CPC分类号： C12Q1/04 ,  G01N27/624 ,  G01N33/497

摘要： Bacteria can be identified by analyzing a data stream that is obtained by processing a sample containing the bacteria, where the data stream has been abstracted to produce a sample vector that characterizes the data stream in a predetermined vector space containing at least one diagnostic cluster, the diagnostic cluster being associated with bacteria of known type, and by determining whether the sample vector rests with the diagnostic cluster, and if the sample rests within the diagnostic cluster, an indication that the bacteria are of the known type can be provided. Similarly, spores can be identified by analyzing a data stream that is obtained by processing a sample containing the spores, where the data stream has been abstracted to produce a sample vector that characterizes the data stream in a predetermined vector space containing at least one diagnostic cluster, the diagnostic cluster being associated with spores of known type, and by determining whether the sample vector rests with the diagnostic cluster, and if the sample rests within the diagnostic cluster, an indication that the spores are of the known type can be provided.

摘要翻译： 可以通过分析通过处理含有细菌的样品获得的数据流来识别细菌，其中数据流已经被抽象以产生在包含至少一个诊断簇的预定向量空间中表征数据流的样本载体， 诊断簇与已知类型的细菌相关联，并且通过确定样品载体是否与诊断簇相关，并且如果样品搁置在诊断簇内，则可以提供细菌是已知类型的指示。 类似地，可以通过分析通过处理含有孢子的样品获得的数据流来识别孢子，其中数据流已被抽象，以产生在包含至少一个诊断簇的预定向量空间中表征数据流的样本向量 ，诊断簇与已知类型的孢子相关联，并且通过确定样品载体是否与诊断簇相关，并且如果样品搁置在诊断簇内，则可以提供孢子是已知类型的指示。