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 present invention provides that ERR? is a breast cancer biomarker of clinical course and treatment sensitivity and, itself, a target for breast cancer treatment. A high ERR? level in breast cancer indicates poor prognosis. Analyzing ERR? expression level along with the status of ER? and ErbB2 can help breast cancer patients make treatment choices. Furthermore, breast cancer can be treated by modulating ERR? activity.

Abstract: The present invention relates to characterizing transcription within cells. In particular, the present invention provides transfected cell arrays (e.g., two-dimensional and/or three-dimensional arrays) and systems, kits and methods utilizing the same (e.g., for transcriptional activity characterization). Compositions and methods of the present invention find use in, among other things, research, drug discovery and clinical (e.g., diagnostic, preventative and therapeutic) applications.

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.

Abstract: The present invention provides that ERR&agr; is a breast cancer biomarker of clinical course and treatment sensitivity and, itself, a target for breast cancer treatment. A high ERR&agr; level in breast cancer indicates poor prognosis. Analyzing ERR&agr; expression level along with the status of ER&agr; and ErbB2 can help breast cancer patients make treatment choices. Furthermore, breast cancer can be treated by modulating ERR&agr; activity.

Abstract: Methods are disclosed to identify differentially expressed genes. In one aspect, one uses subtractive hybridization to enrich for candidate genes, followed by a PCR amplified radiolabeled display of cDNA products of the hybridization. Specially modified primer binding regions are used that can be achieved either through use of trimming plasmids or non-symmetric display plasmids.