Abstract: Methods and systems for identifying biologic subtypes in a biological specimen may include receiving a data set associated with a cohort of biological specimens, determining a potential number of clusters associated with the data set, associating a cluster with one or more data points in the data set, associating a cluster label with the one or more data points in the data set, treating a related cluster as a biologic subtype and associating a biologic subtype with one or more data points with regions of interest in the data set, establishing duster features and metrics in the data set, determining if a new biologic subtype is identified by comparing the biologic subtype associated with the regions of interest with known biologic subtypes, and discovering a new diagnostic, prognostic, predictive, and/or therapeutic biologic subtype by correlating a new biologic subtype with patient, prognostic, predictive, therapeutic and/or clinical trial data.

Abstract: Methods, systems, and devices for classifying a biological sample that include receiving an image of a biological sample and applying one or more algorithms from a data repository to the image, generating a classification of the biological sample based on the outcome of the one or more algorithms applied to the image, and transmitting the classification for presentation on a display or via another medium. The methods, systems, and devices may also include features for developing a data master reference and/or other correlation/translation features to enable comparison of data sets from one platform to another or from one machine to another or from the same machine at different points in time.

Abstract: The methods, devices, and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. The method, devices, and systems may provide a grade or level of development for identified diseases. In addition, the methods, devices and systems may generate a confidence value for the predictive classifications generated, which may, for example be generated in a format to show such confidence value or other feature in a graphical representation (e.g., a color code). Further, the methods, devices and system may aid in the identification and discovery of new classes and tissue sub-types.

Abstract: The methods, systems, and devices may include performing one or more quality tests to determine and/or certify the quality of slides used in infrared imaging. The methods, systems, and devices may certify a quality of a slide when the average quality assessment of the slide is within the established quality value range.

Abstract: The methods, systems, and devices may include performing one or more quality tests to determine and/or certify the quality of slides used in infrared imaging.

Abstract: Methods, systems, and devices for classifying a biological sample that include receiving an image of a biological sample and applying one or more algorithms from a data repository to the image, generating a classification of the biological sample based on the outcome of the one or more algorithms applied to the image, and transmitting the classification for presentation on a display or via another medium. The methods, systems, and devices may also include features for developing a data master reference and/or other correlation/translation features to enable comparison of data sets from one platform to another or from one machine to another or from the same machine at different points in time.