Abstract: Among other things, the present disclosure provides various useful agents. In some embodiments, provided agents can bind to beta-catenin. In some embodiments, the present disclosure provides technologies for modulating beta-catenin functions. In some embodiments, the present disclosure provides technologies for preventing and/or treating conditions, disorders or diseases associated with beta-catenin. In some embodiments, the present disclosure provides designed amino acids and agents which can provide improved properties and/or activities.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.