Abstract: In one embodiment, a computing device includes a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to execute a computer-implemented system configured to manage items in a self-describing data system, wherein the items are associated with a user interface and the computer-implemented system is associated with a first version; execute a third-party computer-implemented system configured to execute an application that accesses the user interface, wherein the third-party computer-implemented system is associated with a second version; and execute a test automation framework (TAF) configured to use the third-party computer-implemented system to perform tests on the user interface associated with the items. The TAF comprises libraries including code specific to the first version of the computer-implemented system and code specific to the second version of the third-party computer-implemented system.

Abstract: Provided herein are methods of identifying a protein capable of binding a ligand, the method comprising: (a) contacting the ligand with two or more samples comprising a plurality of proteins in a solution; (b) separating the proteins bound to the ligand (“bound proteins”) from the proteins that are not bound to the ligand (“unbound proteins”) in each sample; (c) denaturing and digesting the bound proteins to form a plurality of peptides in each sample; (d) quantifying a plurality of molecular features contained in the plurality of peptides in each sample, wherein the molecular features are defined as having a mass to charge ratio, retention time, and peak intensity as measured by mass spectrometry; and (e) ranking the molecular features that exhibit a statistically significant difference in quantity between the samples contacted with the ligand and a sample that is not contacted with the ligand (“statistically significant molecular feature”).

Abstract: Provided herein are methods of identifying a protein capable of binding a ligand, the method comprising: (a) contacting the ligand with two or more samples comprising a plurality of proteins in a solution; (b) separating the proteins bound to the ligand (“bound proteins”) from the proteins that are not bound to the ligand (“unbound proteins”) in each sample; (c) denaturing and digesting the bound proteins to form a plurality of peptides in each sample; (d) quantifying a plurality of molecular features contained in the plurality of peptides in each sample, wherein the molecular features are defined as having a mass to charge ratio, retention time, and peak intensity as measured by mass spectrometry; and (e) ranking the molecular features that exhibit a statistically significant difference in quantity between the samples contacted with the ligand and a sample that is not contacted with the ligand (“statistically significant molecular feature”).

Abstract: An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

Abstract: After prepared biological samples have been submitted to liquid-chromatography/mass spectrometry equipment, digital images are produced that show variations. Some of these variations may be of interest while others are not of interest. Variations in regions of interest can be correlated and correlation scores produced to classify biological features aid in scientific discovery. Shape properties of variations can also be calculated by geometric scores. A microalignment method aids the correlation calculation without resorting to macroalignment.

Abstract: An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

Abstract: An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

Abstract: A technique for finding paired isotope groups of peptides, metabolic materials, or other materials is executed without having to identify features. Any suitable isotopic labeling methods, such as SILAC or ICAT, can be used. The technique can identify isotope pairs by pairing heavy and light labeled peptides based on mono-isotopes. The technique searches for isotope groups that have retention time and mass/charge within given tolerances, adjustable by users. Multiple label sites are supported as well as reverse-labeling to inhibit or reduce biases. Multiple replicates can be merged into a composite image.

Abstract: After prepared biological samples have been submitted to liquid-chromatography/mass spectrometry equipment, digital images are produced that show variations. Some of these variations may be of interest while others are not of interest. Variations in regions of interest can be correlated and correlation scores produced to classify biological features aid in scientific discovery. Shape properties of variations can also be calculated by geometric scores. A microalignment method aids the correlation calculation without resorting to macroalignment.

Abstract: An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

Abstract: An experiment definition system that digitally represents an experiment design. The experiment definition provides the logical structure for data analysis of scans from one or more biological experiments. The experiment definition either directly reflects the experiment design in a one-to-one relationship, or the user customizes the experiment definition. Experiment definitions are stored as a set of instructions in a database of experiment definitions. A user interface for constructing the experiment definition, and for customizing one or more automated analysis pipelines for processing the experiment definitions.

Abstract: An experiment definition system that digitally represents an experiment design. The experiment definition provides the logical structure for data analysis of scans from one or more biological experiments. The experiment definition either directly reflects the experiment design in a one-to-one relationship, or the user customizes the experiment definition. Experiment definitions are stored as a set of instructions in a database of experiment definitions. A user interface for constructing the experiment definition, and for customizing one or more automated analysis pipelines for processing the experiment definitions.

Abstract: A system, method, and computer program product for enhanced computer-aided analysis of biological response data is disclosed. In a preferred embodiment, biological datasets are graphically selected by a user from a first active biological viewer window on a user computer display and projected onto one or more other active biological viewers on the display. The selected data is highlighted in the destination biological viewers using contrast or color differentiation from other data appearing in the destination windows. In another preferred embodiment, hierarchical cluster trees from biological signal profiles are presented in a hyperbolic display fashion. In another preferred embodiment, biological menu and submenu items utilized by the user computer are not stored in the user computer, but rather are stored a central biological response database.

Abstract: Systems, methods, and computer program products for analysis of biological response data are disclosed. Biological datasets are selected from an active biological viewer window on a computer display and projected onto one or more other active biological viewers on the display. The selected data is highlighted in the destination biological viewers using contrast or color differentiation from other data appearing in the destination windows. Systems, methods, and computer program products for displaying hierarchical cluster trees from biological signal profiles in a hyperbolic display fashion are disclosed. Systems, methods, and computer program products are disclosed for precomputing correlation data between biological signal profile data when the experiments are added to a biological response database, thereby eliminating the need for real time computation of correlation coefficients by a user computer.

Abstract: A system, method, and computer program product for enhanced computer-aided analysis of biological response data is disclosed. In a preferred embodiment, biological datasets are graphically selected by a user from a first active biological viewer window on a user computer display and projected onto one or more other active biological viewers on the display. The selected data is highlighted in the destination biological viewers using contrast or color differentiation from other data appearing in the destination windows. In another preferred embodiment, hierarchical cluster trees from biological signal profiles are presented in a hyperbolic display fashion. In another preferred embodiment, biological menu and submenu items utilized by the user computer are not stored in the user computer, but rather are stored a central biological response database.

Abstract: A system, method, and computer program product for enhanced computer-aided analysis of biological response data is disclosed. In a preferred embodiment, biological datasets are graphically selected by a user from a first active biological viewer window on a user computer display and projected onto one or more other active biological viewers on the display. The selected data is highlighted in the destination biological viewers using contrast or color differentiation from other data appearing in the destination windows. In another preferred embodiment, hierarchical cluster trees from biological signal profiles are presented in a hyperbolic display fashion. In another preferred embodiment, biological menu and submenu items utilized by the user computer are not stored in the user computer, but rather are stored a central biological response database.