Abstract: Systems and methods of determining pre-quantitation attributes of biological samples using post-quantitation attributes of those samples is disclosed. By altering a set of biological samples in a measurable way before running the set through an instrument (e.g., a mass spectrometer), a model can be developed that enables determination of the unknown pre-quantitation attributes in other biological samples as a function of post-quantitation attributes.

Abstract: Response to treatment of an inflammatory condition can be predicted based on characteristics of one or more markers from a subject. The markers can include expressions of nucleotide sequences identified herein and of combinations thereof. A response value can be calculated based on characteristics (e.g., expression levels) of one or more of the markers, as well as other characteristics of the subject, such as baseline clinical data. The treatment can be administered when the response value is beyond a threshold.

Abstract: Feature selection methods and processes that facilitate reduction of model components available for iterative modeling. It has been discovered that methods of eliminating model components that do not meaningfully contribute to a solution can be preliminarily discovered and discarded, thereby dramatically decreasing computational requirements in iterative programming techniques. This development unlocks the ability of iterative modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.

Abstract: Feature selection methods and processes that facilitate reduction of model components available for iterative modeling. It has been discovered that methods of eliminating model components that do not meaningfully contribute to a solution can be preliminarily discovered and discarded, thereby dramatically decreasing computational requirements in iterative programming techniques. This development unlocks the ability of iterative modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.

Abstract: Systems and methods of determining pre-quantitation attributes of biological samples using post-quantitation attributes of those samples is disclosed. By altering a set of biological samples in a measurable way before running the set through an instrument (e.g., a mass spectrometer), a model can be developed that enables determination of the unknown pre-quantitation attributes in other biological samples as a function of post-quantitation attributes.

Abstract: This invention comprises a method of simulating an ecological environment, where digital agents within the environment are capable of processing data, and agents that successfully process data are permitted to reproduce to generate new algorithms. The invention is a groundbreaking advance in artificial intelligence and machine learning and enables processes that were once considered computationally impossible.

Abstract: Feature selection methods and processes that facilitate reduction of model components available for iterative modeling. It has been discovered that methods of eliminating model components that do not meaningfully contribute to a solution can be preliminarily discovered and discarded, thereby dramatically decreasing computational requirements in iterative programming techniques. This development unlocks the ability of iterative modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.

Abstract: Systems and methods of determining pre-quantitation attributes of biological samples using post-quantitation attributes of those samples is disclosed. By altering a set of biological samples in a measurable way before running the set through an instrument (e.g., a mass spectrometer), a model can be developed that enables determination of the unknown pre-quantitation attributes in other biological samples as a function of post-quantitation attributes.

Abstract: Feature selection methods and processes that facilitate reduction of model components available for iterative modeling. It has been discovered that methods of eliminating model components that do not meaningfully contribute to a solution can be preliminarily discovered and discarded, thereby dramatically decreasing computational requirements in iterative programming techniques. This development unlocks the ability of iterative modeling to be used to solve complex problems that, in the past, would have required computation time on orders of magnitude too great to be useful.