Abstract: Techniques and apparatus for performing target compound detection processes are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to implement a targeted compound detection process, for example, by receiving raw data from analysis of a sample via an analytical device, generating cumulative data from the raw data, receiving compound specification information associated with the sample, and determining quantified compound information via performing targeted compound detection based on the cumulative data and the compound specification information to determine. Other embodiments are described.

Abstract: Exemplary embodiments provide computer-implemented methods, mediums, and apparatuses configured to perform targeted isotope clustering. A mass spectrum for a sample may be obtained from an analytical laboratory instrument, and a set of peaks within the mass spectrum may be identified. A list of fragments expected to be potentially present in the sample may be obtained, and a set of predicted peaks may be generated from the list. The spectrum may be searched for the predicted peaks to determine if any combination of the peaks present in the spectrum match the expected fragment patterns. Accordingly, isotope (charge) clusters may be built in a targeted way using a priori knowledge to target the matches. As a result, spectrum analysis can be done more quickly and efficiently than in conventional systems that use neutral or untargeted matching, and the matches can be made more accurately.

Abstract: Techniques and apparatus for performing target compound detection processes are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to implement a targeted compound detection process, for example, by receiving raw data from analysis of a sample via an analytical device, generating cumulative data from the raw data, receiving compound specification information associated with the sample, and determining quantified compound information via performing targeted compound detection based on the cumulative data and the compound specification information to determine. Other embodiments are described.

Abstract: The present invention relates principally to the statistical analysis of protein separation patterns. Specifically, the invention provides a method of performing operations on protein samples for the analysis of separation patterns, the method including separating a plurality of protein samples to produce a plurality of respective separation patterns; forming a representation of each separation pattern; building an importance map by iteratively recording performance values of classification models against locations in the importance map which correspond to locations in representations of separation patterns of data points used to build the classification models; and processing the importance map to identify important areas. Parts of the map which include high performance values indicate regions of the separation pattern which are statistically important in predicting a classification of the pattern.

Abstract: The invention relates principally to the statistical analysis of protein separation patterns. The invention solves the problems associated with producing models which are predictive of classification using unreduced data. The invention provides a method of analysing a representation of a separation pattern, the representation including a neighborhood representing a region of the separation pattern, the neighborhood including a plurality of data points, the method comprising augmenting data by representing the entire region using each data point of the neighborhood; and building a classification model using some or all of the data points.

Abstract: The invention relates principally to the statistical analysis of protein separation patterns. The invention solves the problems associated with producing models which are predictive of classification using unreduced data. The invention provides a method of analysing a representation of a separation pattern, the representation including a neighborhood representing a region of the separation pattern, the neighborhood including a plurality of data points, the method comprising augmenting data by representing the entire region using each data point of the neighborhood; and building a classification model using some or all of the data points.

Abstract: The present invention relates principally to the statistical analysis of protein separation patterns. The invention provides a method of analysing representations of separation patterns, the method comprising iteratively performing the steps of (1) building a classification model based on a subset of data points selected from one or more representations, (2) assessing the performance of the model to determine whether its performance is within a desired range, and (3) adjusting the size of the subset until the performance of the model falls within the desired range.

Abstract: The present invention relates principally to the statistical analysis of protein separation patterns. Specifically, the invention provides a method of performing operations on protein samples for the analysis of separation patterns, the method including separating a plurality of protein samples to produce a plurality of respective separation patterns; forming a representation of each separation pattern; building an importance map by iteratively recording performance values of classification models against locations in the importance map which correspond to locations in representations of separation patterns of data points used to build the classification models; and processing the importance map to identify important areas. Parts of the map which include high performance values indicate regions of the separation pattern which are statistically important in predicting a classification of the pattern.