Abstract: Methods for processing hyphenated chromatographic data to delineate components of a sample are disclosed. According to one aspect, the method includes obtaining hyphenated chromatographic data points for a sample, each data point comprising at least three dimensions, one of the dimensions being a continuous dimension, and subjecting at least a portion of the data points to an algorithm that organizes the data points into discrete clusters according to the data points' continuous dimension values by starting at either a smallest or largest value and delineating at a largest gap between adjacent values within a predetermined resolution window, wherein the resulting clusters are of varying width of less than or equal to the width of the resolution window and wherein at least some of the clusters are indicative of components of the sample.

Abstract: Methods are provided for non-targeted processing of hyphenated chromatographic data, such that even when the data represent a complex mixture of unknown components, a comprehensive number of the components are detected with minimal information loss and introduction of error. Processing hyphenated chromatographic data by the present methods is compatible with use of subsequent algorithms for chromatographic information extraction, and enhances the accuracy of a subsequent peak/component picking program. The present methods comprise subjecting data collected using a hyphenated chromatographic technique like GC-MS or LC-MS, i.e., data having a continuous or non-discrete dimension (e.g., mass), to an algorithm that organizes the data into discrete groups (clusters) of the continuous dimension. Grouping using the present methods results in consecutive clusters of continuous dimensions having varying width less than or equal to that of the instrument resolution window.

Abstract: The present invention provides methods and systems for organizing complex and disparate data. More specifically, the present invention provides methods and systems for organizing complex and disparate data into coherent data sets. Coherent data sets resulting from the methods and systems of the present invention serve as models for biological systems. Methods and systems for integrating data and creating coherent data sets are useful for numerous biological applications, such as, for example, determining gene function, identifying and validating drug and pesticide targets, identifying and validating drug and pesticide candidate compounds, profiling drug and pesticide compounds, producing a compilation of health or wellness profiles, determining compound site(s) of action, identifying unknown samples, and numerous other applications in the agricultural, pharmaceutical, forensic, and biotechnology industries.

Abstract: The present invention provides methods and systems for organizing complex and disparate data. More specifically, the present invention provides methods and systems for organizing complex and disparate data into coherent data sets. Coherent data sets resulting from the methods and systems of the present invention serve as models for biological systems. Methods and systems for integrating data and creating coherent data sets are useful for numerous biological applications, such as, for example, determining gene function, identifying and validating drug and pesticide targets, identifying and validating drug and pesticide candidate compounds, profiling drug and pesticide compounds, producing a compilation of health or wellness profiles, determining compound site(s) of action, identifying unknown samples, and numerous other applications in the agricultural, pharmaceutical, forensic, and biotechnology industries.

Abstract: The present invention provides methods and systems for organizing complex and disparate data. More specifically, the present invention provides methods and systems for organizing complex and disparate data into coherent data sets. Coherent data sets resulting from the methods and systems of the present invention serve as models for biological systems. Methods and systems for integrating data and creating coherent data sets are useful for numerous biological applications, such as, for example, determining gene function, identifying and validating drug and pesticide targets, identifying and validating drug and pesticide candidate compounds, profiling drug and pesticide compounds, producing a compilation of health or wellness profiles, determining compound site(s) of action, identifying unknown samples, and numerous other applications in the agricultural, pharmaceutical, forensic, and biotechnology industries.