Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

Abstract: Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets, and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

Abstract: Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets, and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

Abstract: Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Abstract: A number of methods and computer systems related to mass spectrometric data analysis are disclosed. Adoption of the disclosure herein facilitates automated, high throughput, rapid analysis of complex datasets such as datasets generated through mass spectrometric analysis, so as to reduce or eliminate the need for oversight in the analysis process while rapidly yielding accurate results. In some cases, identification of a health condition indicator is carried out based on information relating a predetermined association between an input parameter and a health condition indicator.

Abstract: Described herein are systems and methods for developing and utilizing assays for assessing health status such as colorectal cancer.

Abstract: Systems, methods, filters, and devices are disclosed for quality control monitoring for samples collected and stored on filters. Sample collection devices and filters have markers that act as quality control indicators for one or more procedures involving a sample such as collection, storage, transport, and elution. Practice of the disclosure herein allows for sample evaluation to enhance downstream applications such as ongoing monitoring of a patients health status through the accurate, repeatable measurement of markers in a sample. Reference biomarkers can be used to enhance assessment of health status. In some cases, the present disclosure enables the detection of a disease signal and assessment of disease status through the measurement and analysis of biomarkers in a sample.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: Databases methods and reagents are disclosed for the generation of large amounts of biomarker data from readily obtained sample such as dried plasma spots, and for uses of such databases in the development of patient categorization or detection of changes in a patient's health status over time.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of advanced adenoma and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

Abstract: The disclosed methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management. The methodology provided herein is intended to detect technical variations and to allow for data normalization and enhance signal detection and build predictive proteins profiles of disease status and response.