Correlogic Systems, Inc.

The United States of America, As Represented by the Secretary, Department of Health and Human Services

Other

Ben A Hitt

Peter J Levine

Timothy A Coleman

Emanuel F Petricoin, Iii

Lance A Liotta

Tzong-Hao Chen

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.

Method of diagnosing biological states through the use of a centralized, adaptive model, and remote sample processing

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.

Heuristic method of classification

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.

Quality assurance/quality control for electrospray ionization processes

The invention relates to a method of quality assurance/quality control for high-throughput bioassay processes. The method permits monitoring of an entire system for obtaining spectral data from biological samples. Generally, the method includes generating a bioassay process model, comparing a test sample against the bioassay process model. The bioassay process model may be based on the position of a centroid in n-dimensional space. The comparing may include comparing the location of a centroid associated with the test model against the centroid associated with the control model to determine the distance between the two centroids. By generating a trend plot of the distance between the centroid associated with the test sample and the centroid associated with the control model, overall system performance may be monitored over time.

Quality assurance for high-throughput bioassay methods

Quality assurance/quality control for high throughput bioassay process

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.

Process for discriminating between biological states based on hidden patterns from biological data

An adaptive fuzzy feature mapping (AFFM) technique provides a method for identifying and matching a new data pattern against a set of known data patterns using a combination of distance measurements and fuzzy logic functions. Known data patterns are stored as organized nodes in a pattern map wherein each organized node is defined by one or more attribute coefficients. As distance measurement is computed between a new data pattern and each organized node of the pattern map using distance measurement wherein the organized node having the smallest distance measurement to the new data pattern receives the highest ranking. Traversing the organized nodes according to the ranking, the new data pattern is compared to each organized node using fuzzy logic functions. If the new data pattern matches an organized node based on an acceptable degree of fuzziness, the attribute coefficients of the organized node are updated to reflect those coefficients of the new data pattern. If the new data pattern does not match any of the organized nodes in the pattern map, a new organized node is created in the pattern map representing the attribute coefficients of the new data pattern.

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Gaithersburg, MD, United States of America

Ben A Hitt