Patents by Inventor Isabelle Guyon

Isabelle Guyon has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20090226915
    Abstract: Biomarkers are identified by analyzing gene expression data using support vector machines (SVM) to rank genes according to their ability to separate prostate cancer from normal tissue. Expression products of identified genes are detected in patient samples, including prostate tissue, serum, semen and urine, to screen, predict and monitor prostate cancer.
    Type: Application
    Filed: January 6, 2009
    Publication date: September 10, 2009
    Applicant: HEALTH DISCOVERY CORPORATION
    Inventor: Isabelle Guyon
  • Publication number: 20090215024
    Abstract: Biomarkers are identified by analyzing gene expression data using support vector machines (SVM) to rank genes according to their ability to separate prostate cancer from normal tissue. Proteins expressed by identified genes are detected in patient samples to screen, predict and monitor prostate cancer.
    Type: Application
    Filed: February 4, 2008
    Publication date: August 27, 2009
    Applicant: HEALTH DISCOVERY CORPORATION
    Inventor: ISABELLE GUYON
  • Publication number: 20090215058
    Abstract: Biomarkers are identified by analyzing gene expression data using support vector machines (SVM) to rank genes according to their ability to separate prostate cancer from normal tissue. Expression products of identified genes are detected in patient samples, including prostate tissue, serum, semen and urine, to screen, predict and monitor prostate cancer.
    Type: Application
    Filed: December 4, 2008
    Publication date: August 27, 2009
    Applicant: HEALTH DISCOVERY CORPORATION
    Inventor: Isabelle Guyon
  • Patent number: 7542959
    Abstract: Identification of a determinative subset of features from within a large set of features is performed by training a support vector machine to rank the features according to classifier weights, where features are removed to determine how their removal affects the value of the classifier weights. The features having the smallest weight values are removed and a new support vector machine is trained with the remaining weights. The process is repeated until a relatively small subset of features remain that is capable of accurately separating the data into different patterns or classes. The method is applied for selecting the smallest number of genes that are capable of accurately distinguishing between medical conditions such as cancer and non-cancer.
    Type: Grant
    Filed: August 21, 2007
    Date of Patent: June 2, 2009
    Assignee: Health Discovery Corporation
    Inventors: Stephen Barnhill, Isabelle Guyon, Jason Weston
  • Patent number: 7542947
    Abstract: The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs).
    Type: Grant
    Filed: October 30, 2007
    Date of Patent: June 2, 2009
    Assignee: Health Discovery Corporation
    Inventors: Isabelle Guyon, Edward P. Reiss, René Doursat, Jason Aaron Edward Weston, David D. Lewis
  • Patent number: 7475048
    Abstract: A computer-implemented method is provided for ranking features within a large dataset containing a large number of features according to each feature's ability to separate data into classes. For each feature, a support vector machine separates the dataset into two classes and determines the margins between extremal points in the two classes. The margins for all of the features are compared and the features are ranked based upon the size of the margin, with the highest ranked features corresponding to the largest margins. A subset of features for classifying the dataset is selected from a group of the highest ranked features. In one embodiment, the method is used to identify the best genes for disease prediction and diagnosis using gene expression data from micro-arrays.
    Type: Grant
    Filed: November 7, 2002
    Date of Patent: January 6, 2009
    Assignee: Health Discovery Corporation
    Inventors: Jason Weston, André Elisseeff, Bernhard Schölkopf, Fernando Perez-Cruz, Isabelle Guyon
  • Patent number: 7444308
    Abstract: The data mining platform comprises a plurality of system modules (500, 550), each formed from a plurality of components. Each module has an input data component (502, 552), a data analysis engine (504, 554) for processing the input data, an output data component (506, 556) for outputting the results of the data analysis, and a web server (510) to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality.
    Type: Grant
    Filed: June 17, 2002
    Date of Patent: October 28, 2008
    Assignee: Health Discovery Corporation
    Inventors: Isabelle Guyon, Edward P. Reiss, René Doursat, Jason Aaron Edward Weston
  • Publication number: 20080233576
    Abstract: In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score, transductive feature selection and single feature using margin-based ranking. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.
    Type: Application
    Filed: October 30, 2007
    Publication date: September 25, 2008
    Inventors: Jason Weston, Andre Ellisseeff, Bernhard Scholkopf, Fernando Perez-Cruz, Isabelle Guyon
  • Publication number: 20080140592
    Abstract: A model selection method is provided for choosing the number of clusters, or more generally the parameters of a clustering algorithm. The algorithm is based on comparing the similarity between pairs of clustering runs on sub-samples or other perturbations of the data. High pairwise similarities show that the clustering represents a stable pattern in the data. The method is applicable to any clustering algorithm, and can also detect lack of structure. We show results on artificial and real data using a hierarchical clustering algorithm.
    Type: Application
    Filed: October 30, 2007
    Publication date: June 12, 2008
    Inventors: Asa Ben-Hur, Andre Elisseeff, Isabelle Guyon
  • Publication number: 20080097938
    Abstract: The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs).
    Type: Application
    Filed: October 30, 2007
    Publication date: April 24, 2008
    Inventors: Isabelle Guyon, Edward Reiss, Rene Doursat, Jason Weston, David Lewis
  • Publication number: 20080097939
    Abstract: The data mining platform comprises a plurality of system modules, each formed from a plurality of components. Each module has an input data component, a data analysis engine for processing the input data, an output data component for outputting the results of the data analysis, and a web server to access and monitor the other modules within the unit and to provide communication to other units. Each module processes a different type of data, for example, a first module processes microarray (gene expression) data while a second module processes biomedical literature on the Internet for information supporting relationships between genes and diseases and gene functionality. In the preferred embodiment, the data analysis engine is a kernel-based learning machine, and in particular, one or more support vector machines (SVMs).
    Type: Application
    Filed: October 30, 2007
    Publication date: April 24, 2008
    Inventors: Isabelle Guyon, Edward Reiss, Rene Doursat, Jason Weston, David Lewis
  • Patent number: D570105
    Type: Grant
    Filed: July 17, 2007
    Date of Patent: June 3, 2008
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D570107
    Type: Grant
    Filed: July 17, 2007
    Date of Patent: June 3, 2008
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D570110
    Type: Grant
    Filed: July 17, 2007
    Date of Patent: June 3, 2008
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D570111
    Type: Grant
    Filed: July 17, 2007
    Date of Patent: June 3, 2008
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D593320
    Type: Grant
    Filed: November 26, 2007
    Date of Patent: June 2, 2009
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D594652
    Type: Grant
    Filed: July 15, 2008
    Date of Patent: June 23, 2009
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D594654
    Type: Grant
    Filed: November 26, 2007
    Date of Patent: June 23, 2009
    Assignee: S.A.S Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D594655
    Type: Grant
    Filed: July 15, 2008
    Date of Patent: June 23, 2009
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain
  • Patent number: D595050
    Type: Grant
    Filed: November 26, 2007
    Date of Patent: June 30, 2009
    Assignee: S.A.S. Jean Cassegrain
    Inventors: Isabelle Guyon, Sophie Delafontaine, Philippe Cassegrain