Patent application number | Description | Published |
20080233576 | METHOD FOR FEATURE SELECTION IN A SUPPORT VECTOR MACHINE USING FEATURE RANKING - 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 (l | 09-25-2008 |
20090215024 | BIOMARKERS UPREGULATED IN PROSTATE CANCER - 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. | 08-27-2009 |
20090215058 | METHODS FOR SCREENING, PREDICTING AND MONITORING PROSTATE CANCER - 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. | 08-27-2009 |
20090286240 | BIOMARKERS OVEREXPRESSED IN PROSTATE CANCER - 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. | 11-19-2009 |
20090305257 | BIOMARKERS DOWNREGULATED IN PROSTATE CANCER - Biomarkers are identified by analyzing gene expression data using support vector machines (SVM), recursive feature elimination (RFE) and/or linear ridge regression classifiers 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. | 12-10-2009 |
20110078099 | METHOD FOR FEATURE SELECTION AND FOR EVALUATING FEATURES IDENTIFIED AS SIGNIFICANT FOR CLASSIFYING DATA - A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features. The features in the group that have a calculated extremal margin value less than the specified margin value are labeled as falsely significant. | 03-31-2011 |
20110106735 | RECURSIVE FEATURE ELIMINATION METHOD USING SUPPORT VECTOR MACHINES - Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample. | 05-05-2011 |
20110125683 | Identification of Co-Regulation Patterns By Unsupervised Cluster Analysis of Gene Expression Data - A method is provided for unsupervised clustering of gene expression data to identify co-regulation patterns. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on similarity, with the greatest similarity giving the highest correlation score. A distribution of the scores is plotted for each k. The highest value of k that has a distribution that remains concentrated near the highest correlation score corresponds to the number of co-regulation patterns. | 05-26-2011 |
20110184896 | METHOD FOR VISUALIZING FEATURE RANKING OF A SUBSET OF FEATURES FOR CLASSIFYING DATA USING A LEARNING MACHINE - A method for enhancing knowledge discovery from a dataset uses visualization of a subset features within a dataset that provide the best separation of the dataset into classes. One or more classifiers are trained using each subset of features and the success rate of the classifiers in accurately classifying the dataset is calculated. The success rate is converted into a ranking that is represented as a visually distinguishable characteristic. One or more tree structures may be displayed with a node representing each feature, and the visually distinguishable characteristic is used to indicate the scores for each feature subset. Connectors between the nodes may be used to indicate unconstrained and constrained feature sets. Nodes within a constrained path may be substituted for a feature within the preferred, unconstrained path if that feature is impractical to measure. | 07-28-2011 |
20110312509 | BIOMARKERS DOWNREGULATED IN PROSTATE CANCER - Biomarkers are identified by analyzing gene expression data using support vector machines (SVM), recursive feature elimination (RFE) and/or linear ridge regression classifiers 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. | 12-22-2011 |
20120008838 | SYSTEM AND METHOD FOR REMOTE MELANOMA SCREENING - A system and method are provided for diagnosing diseases or conditions from digital images taken by a remote user with a smart phone or a digital camera and transmitted to an image analysis server in communication with a distributed network. The image analysis server includes a trained learning machine for classification of the images. The user-provided image is pre-processed to extract dimensional, shape and color features then is processed using the trained learning machine to classify the image. The classification result is postprocessed to generate a risk score that is transmitted to the remote user. A database associated with the server may include referral information for geographically matching the remote user with a local physician. An optional operation includes collection of financial information to secure payment for analysis services. | 01-12-2012 |
20130297607 | IDENTIFICATION OF PATTERN SIMILARITIES BY UNSUPERVISED CLUSTER ANALYSIS - A method is provided for unsupervised clustering of data to identify pattern similarities. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on similarity, with the greatest similarity giving the highest correlation score. A distribution of the scores is plotted for each k. The highest value of k that has a distribution that remains concentrated near the highest correlation score corresponds to the number of classes having pattern similarities. | 11-07-2013 |
20140018249 | BIOMARKERS FOR SCREENING, PREDICTING, AND MONITORING BENIGN PROSTATE HYPERPLASIA - Gene expression data are analyzed using learning machines such as support vector machines (SVM) and ridge regression classifiers to rank genes according to their ability to distinguish between BPH (benign prostatic hyperplasia) and all other conditions. Results are provided showing the correlation of results obtained using data from two independent studies that took place at different times using different microarrays. Genes are ranked according to area-under-the-curve, false discovery rate and fold change. | 01-16-2014 |