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: 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: 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: 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 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.

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.

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: A method for identifying cancer patients that are likely to be responders or non-responders to a signal transduction pathway inhibitor is described.

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: The present disclosure provides methods, devices and kits that permit large numbers of target biomolecules to be detected simultaneously in samples originating from a multi-sample holder, such as a multi-well plate. One specific example method is a method of making multiple substantial replicas of a biomolecular content of a multi-well sample holder. Devices and kits for carrying out the described methods are also provided.

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: The present disclosure provides methods, devices and kits that permit large numbers of target biomolecules to be detected simultaneously in samples originating from a multi-sample holder, such as a multi-well plate. One specific example method is a method of making multiple substantial replicas of a biomolecular content of a multi-well sample holder. Devices and kits for carrying out the described methods are also provided.

Abstract: The present disclosure is directed to devices, arrays, kits and methods for detecting biomolecules in a tissue section (such as a fresh or archival sample, tissue microarray, or cells harvested by an LCM procedure) or other substantially two-dimensional sample (such as an electrophoretic gel or cDNA microarray) by creating “carbon copies” of the biomolecules eluted from the sample and visualizing the biomolecules on the copies using one or more detector molecules (e.g., antibodies or DNA probes) having specific affinity for the biomolecules of interest. Specific methods are provided for identifying the pattern of biomolecules (e.g., proteins and nucleic acids) in the samples. Other specific methods are provided for the identification and analysis of proteins and other biological molecules produced by cells and/or tissue, especially human cells and/or tissue.

Abstract: Provided herein are methods for preliminary analysis of suspect samples, which can be used in triaging possible contaminated sites (e.g., sites contaminated or thought to be contaminated by biowarfare agents). In some embodiments, the methods involve testing for the presence of protein in the suspect sample; optionally, the sample can also be tested for the presence of sugar, and/or for pH determination. Specific embodiment methods are carried out in tubes or other reaction vessels, others are carried out in a pad format, and still others are carried out in a test strip format. Kits for carrying out the described methods are also provided.

Abstract: Provided herein are methods for preliminary analysis of suspect samples, which can be used in triaging possible contaminated sites (e.g., sites contaminated or thought to be contaminated by biowarfare agents). In some embodiments, the methods involve testing for the presence of protein in the suspect sample; optionally, the sample can also be tested for the presence of sugar, and/or for pH determination. Specific embodiment methods are carried out in tubes or other reaction vessels, others are carried out in a pad format, and still others are carried out in a test strip format. Kits for carrying out the described methods are also provided.