Abstract: Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Abstract: Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Abstract: Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Abstract: The present disclosure provides oligonucleotide adapter compositions, methods, and systems for improved resolution of 5hmC sequencing useful for improving nucleic acid sequencing library quality and nucleic acid methylation profiling. Also provided are methods of applying the improved oligonucleotide adapters and sequencing methods for machine learning classifier generation, and detecting cell proliferative disorders such as cancer. Methods of applying targeted nucleic acid enrichment with methods of applying the improved oligonucleotide adapters and sequencing methods for improving nucleic acid sequencing library quality and nucleic acid methylation profiling are also provided.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Methods and systems provided herein address current limitations of bisulfite-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Methods and systems provided herein address current limitations of bisulfate-based methylation sequencing by improving the quality and accuracy of nucleic acid methylation sequencing and uses thereof for detection of disease. Methods that include minimally-destructive conversion methods for methylation sequencing as well as specialized UMI adapters provide for improved quality of sequencing libraries and sequencing information. Greater accuracy and more complete methylation-state information permits higher quality feature generation for use in machine learning models and classifier generation.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: The inventors discovered that the ErbB2 signal transduction pathway can activate ERR? by inducing its phosphorylation. Based on this discovery, the present invention provides methods for determining whether a breast cancer patient is likely to respond to hormonal-blockade therapy or ErbB2-based therapy, methods for determining prognosis of breast cancer patients, methods for treating breast cancer, and methods for identifying agents that can modulate ERR? phosphorylation.