Abstract: Embodiments described herein generally relate to technologies for analyzing peptide structures for diagnosing and/or treating a disease state advancing through a disease progression. A non-limiting example of a method relating to the technologies described in the subject application may include receiving peptide structure data corresponding to the biological sample obtained from the subject, identifying a peptide structure profile, and diagnosing a disease state within a disease progression. The example may further include generating a diagnosis output relating to the disease state. In at least some cases, the peptide structure profile may include glycosylated peptides, aglycosylated peptides, or both.

Abstract: The present disclosure encompasses systems, methods, and compositions for diagnosing a subject for an AA or colorectal cancer (CRC) disease state by ascertaining the presence of certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from the subject. Specific embodiments encompass methods of measuring certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from subjects known to have or suspected of having an AA or CRC disease state or subjects undergoing routine health care maintenance for possible presence of an AA or CRC disease state. The disclosure provides systems, methods, and compositions to identify subjects at-risk for CRC or AA and increases subject colonoscopy compliance, in specific embodiments.

Abstract: The present disclosure encompasses systems, methods, and compositions for diagnosing a subject for an AA or colorectal cancer (CRC) disease state by ascertaining the presence of certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from the subject. Specific embodiments encompass methods of measuring certain one or more glycosylated or aglycosylated peptides in liquid biopsy samples from subjects known to have or suspected of having an AA or CRC disease state or subjects undergoing routine health care maintenance for possible presence of an AA or CRC disease state. The disclosure provides systems, methods, and compositions to identify subjects at-risk for CRC or AA and increases subject colonoscopy compliance, in specific embodiments.

Abstract: A system and method for automated detection of the presence or absence of a quantity based on intensities expressed in terms of, or derived from frequency or time dependent data. According to one example intensities from mass spectrometry are identified using a non-linear mathematical model, such as an artificial neural network trained to find start and stop peaks of an intensity, from which an abundance may be determined.

Abstract: A system and method for automated detection of the presence or absence of a quantity based on intensities expressed in terms of, or derived from frequency or time dependent data. According to one example intensities from mass spectrometry are identified using a non-linear mathematical model, such as an artificial neural network trained to find start and stop peaks of an intensity, from which an abundance may be determined.

Abstract: Embodiments described herein generally relate to technologies for analyzing peptide structures for diagnosing and/or treating a disease state advancing through a disease progression. A non-limiting example of a method relating to the technologies described in the subject application may include receiving peptide structure data corresponding to the biological sample obtained from the subject, identifying a peptide structure profile, and diagnosing a disease state within a disease progression. The example may further include generating a diagnosis output relating to the disease state. In at least some cases, the peptide structure profile may include glycosylated peptides, aglycosylated peptides, or both.

Abstract: A system and method for automated detection of the presence or absence of a quantity based on intensities expressed in terms of, or derived from frequency or time dependent data. According to one example intensities from mass spectrometry are identified using a non-linear mathematical model, such as an artificial neural network trained to find start and stop peaks of an intensity, from which an abundance may be determined.

Abstract: Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glvcomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.

Abstract: Embodiments described herein generally relate to technologies for analyzing peptide structures for diagnosing and/or treating a disease state advancing through a disease progression. A non-limiting example of a method relating to the technologies described in the subject application may include receiving peptide structure data corresponding to the biological sample obtained from the subject, identifying a peptide structure profile, and diagnosing a disease state within a disease progression. The example may further include generating a diagnosis output relating to the disease state. In at least some cases, the peptide structure profile may include glycosylated peptides, aglycosylated peptides, or both.

Abstract: Set forth herein are methods useful for identifying disease biomarkers, particularly for diseases such as clear cell renal cell carcinoma (ccRCC). In some examples, the methods set forth herein are useful for monitoring the prognosis of patients having a disease such as ccRCC.

Abstract: Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glycomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.

Abstract: Embodiments described herein generally relate to systems and methods for processing mass spectrometry samples. Aspects of the disclosure include systems and methods for processing samples. Additionally, embodiments of the disclosure can also include systems and methods for sample analysis. Various embodiments include data analysis systems and methods for comparing data across samples and sample runs. Data analysis systems can run normalization methods for normalizing raw abundance mass spectrometry data. In some aspects, the normalized data can be used as input for predictive models.

Abstract: Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as but not limited to, cancer (e.g., ovarian), an autoimmune disease, fibrosis and aging conditions. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning algorithms.

Abstract: Set forth herein are glycopeptide biomarkers useful for diagnosing diseases and conditions, such as ovarian cancer. Also set forth herein are methods of generating glycopeptide biomarkers and methods of analyzing glycopeptides using mass spectroscopy. Also set forth herein are methods of analyzing glycopeptides using machine learning systems.

Abstract: Provided herein are methods, devices, and kits for identifying glycosylated polypeptide biomarkers and signatures for progression of a disease or a condition, such as cancer, or and response of the disease or condition to a treatment, such as treatment with immune checkpoint blockade for cancer. Provided herein are methods of generating glycosylated polypeptide biomarkers and methods of analyzing glycosylated polypeptides using mass spectrometry. Provided herein are methods of validating a model using glycosylated polypeptides for predicting the disease or condition or for making treatment recommendation.

Abstract: Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glvcomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.

Abstract: Provided herein are methods for identifying new biomarkers for various diseases using proteomics, peptidomics, metabolics, proteoglycomics, glvcomics, mass spectrometry and machine learning. The present disclosure also provides glycopeptides as biomarkers for various diseases such as cancer and autoimmune diseases.