NOVELTY - Physiological characterization in a subject comprises (a) obtaining a physiological sample of the subject, (b) determining biomarker measures of biomarkers in the sample, and (c) classifying the sample based on the biomarker measures using a classification system, where the classification of the sample correlates to a physiologic state or condition, or changes in a disease state in the subject.
USE - The method is useful for: physiological characterization in a subject; diagnosing reactive airway disease; and diagnosing non-small cell lung cancer in a subject, where the disease state is lung disease including non-small cell cancer and reactive airway disease including asthma.
DETAILED DESCRIPTION - INDEPENDENT CLAIMS are included for:
(1) a method-I of diagnosing a lung disease in a subject comprising, step (a) as above per se (b1) determining biomarker measures of the biomarkers that assist in discriminating between the indication of reactive airway disease and non-small cell lung cancer, the biomarkers indicative of reactive airway disease, and the biomarkers indicative of non-small cell lung cancer, in the sample, where the biomarkers are not identical, step (c) as above per se, where the classification of the sample assists in discriminating between the indication of (i) reactive airway disease and non-small cell lung cancer, (ii) presence or absence of reactive airway disease, and (iii) presence or absence of non-small cell lung cancer, and (d) determining the subject to have (1) reactive airway disease, (2) non-small cell lung cancer, or (3) absence of disease, depending on which condition is found in two of the three classifications;
(2) a method-II of classifying test data, comprising (Aa) receiving test data comprising a biomarker measure for each biomarker of the set of biomarkers in a human test subject, (Ba) evaluating the test data using an electronic representation of a support vector machine or AdaBoost classifier trained using an electronically stored set of training data vectors, (Ca) each training data vector representing an individual human and comprising a biomarker measure of each biomarker of the set of biomarkers for the respective human, (Da) each training data vector further comprising a classification with respect to a disease state of the respective human, and (Ea) outputting a classification of the human test subject based on the evaluating step, where the test data comprises the biomarker measures of each of a set of biomarkers, or (Ea) accessing an electronically stored set of training data vectors, step (Ca)-(Da) as above per se, training an electronic representation of a support vector machine using the electronically stored set of training data vectors, receiving test data comprising a plurality of biomarker measures for the set of biomarkers in a human test subject, evaluating the test data using the electronic representation of the support vector machine, and outputting a classification of the human test subject based on the evaluating step, or (Ea) as above per se, each training data vector representing an individual human and comprising a biomarker measure of each biomarker of the set of biomarkers for the respective human, step (Da) as above per se, selecting a subset of biomarkers from the set of biomarkers, training an electronic representation of a support vector machine using the data from the subset of biomarkers of the electronically stored set of training data vectors; and
(3) a system for classifying test data.
