Abstract: Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients.

Abstract: A method of data interpretation from a multiplex cancer assay is described. The aggregate normalized score from the assay is transformed to a quantitative risk score quantifying a human subject's increased risk for the presence of cancer as compared to the known prevalence of the cancer in the population before testing the subject.

Abstract: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.

Abstract: Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership and/or a category with a time range for follow up testing or reclassification with newly measured input factors.

Abstract: Embodiments of the present invention relate generally to non-invasive methods and tests that measure biomarkers (e.g., tumor antigens) and collect clinical parameters from patients, and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient has a disease, relative to a patient population or a cohort population. In one embodiment, a classifier is generated using a machine learning system based on training data from retrospective data and subset of inputs (e.g. at least two biomarkers and at least one clinical parameter), wherein each input has an associated weight and the classifier meets a predetermined Receiver Operator Characteristic (ROC) statistic, specifying a sensitivity and a specificity, for correct classification of patients.

Abstract: A lung cancer biomarker panel comprising an microRNA (miRNA) lung cancer biomarker and at least one additional lung cancer biomarker selected from a tumor protein (TP) lung cancer biomarker and/or a autoantibody (AAB) lung cancer biomarker is provided herein and methods for screening patients for lung cancer. The present lung cancer biomarker panel provides an improvement in sensitivity and diagnostic accuracy for lung cancer as compared to a lung cancer biomarker panel without the miRNA biomarkers.

Abstract: Embodiments of the present invention relate generally to non-invasive methods and diagnostic tests that measure biomarkers (e.g., tumor antigens), clinical parameters and computer-implemented machine learning methods, apparatuses, systems, and computer-readable media for assessing a likelihood that a patient with radiographic apparent pulmonary nodules are malignant as compared to benign, relative to a patient population or a cohort population. By utilizing algorithms generated from the biomarker levels (e.g., tumor antigens) from large volumes of longitudinal or prospectively collected blood samples (e.g., real world data from one or more regions where blood based tumor biomarker cancer screening is commonplace) together with one or more clinical parameters (e.g. age, smoking history, disease signs or symptoms) a risk level of that patient having malignant pulmonary nodules is provided.