| HEALTH DISCOVERY CORPORATION Patent applications |
| Patent application number | Title | Published |
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| 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 |
| 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 |
| 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 |
| 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 |
| 20110119213 | SUPPORT VECTOR MACHINE - RECURSIVE FEATURE ELIMINATION (SVM-RFE) - 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. | 05-19-2011 |
| 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 |
| 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 |
| 20100318482 | Kernels for Identifying Patterns in Datasets Containing Noise or Transformation Invariances - Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map. | 12-16-2010 |
| 20100205124 | SUPPORT VECTOR MACHINE-BASED METHOD FOR ANALYSIS OF SPECTRAL DATA - Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered. | 08-12-2010 |
| 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 |
| 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 |
| 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 |
| 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 |
| 20090204557 | Method and System for Analysis of Flow Cytometry Data Using Support Vector Machines - An automated method and system are provided for receiving an input of flow cytometry data and analyzing the data using one or more support vector machines to generate an output in which the flow cytometry data is classified into two or more categories. The one or more support vector machines utilizes a kernel that captures distributional data within the input data. Such a distributional kernel is constructed by using a distance function (divergence) between two distributions. In the preferred embodiment, a kernel based upon the Bhattacharya affinity is used. The distributional kernel is applied to classification of flow cytometry data obtained from patients suspected having myelodysplastic syndrome. | 08-13-2009 |
