Abstract: A method and system for predicting liver injury in vivo due to hepatocyte damage by a test compound are provided. The method includes acquiring images of fluorescently stained cells obtained from a cell culture in which the cells have been treated with a dose-range of at least the test compound and its vehicle. The cells may be hepatic cells including primary or immortalized hepatocytes, hepatoma cells or induced pluripotent stem cell-derived hepatocyte-like cells. The acquired images are segmented. The method further includes extracting and analyzing one or more phenotypic features from the segmented images, wherein the one or more phenotypic features are selected from the group of intensity, textural, morphological, or ratiometric features consisting of (a) features of DNA, (b) features of RELA (NF-KB p65), and (c) features of actin filaments at different subcellular regions and d) features of cellular organelles and their substructures in the segmented images.

Abstract: A method and system for predicting liver injury in vivo due to hepatocyte damage by a test compound are provided. The method includes acquiring images of fluorescently stained cells obtained from a cell culture in which the cells have been treated with a dose-range of at least the test compound and its vehicle. The cells may be hepatic cells including primary or immortalized hepatocytes, hepatoma cells or induced pluripotent stem cell-derived hepatocyte-like cells. The acquired images are segmented. The method further includes extracting and analyzing one or more phenotypic features from the segmented images, wherein the one or more phenotypic features are selected from the group of intensity, textural, morphological, or ratiometric features consisting of (a) features of DNA, (b) features of RELA (NF-KB p65), and (c) features of actin filaments at different subcellular regions and d) features of cellular organelles and their substructures in the segmented images.

Abstract: The present invention provides methods for the prediction of in vivo cell-specific toxicity of a compound that combines high-throughput imaging of cultured cells, quantitative phenotypic profiling, and machine learning methods. More particularly, the invention provides a method for the prediction of in vivo renal proximal tubular-, bronchial-epithelial-, and alveolar-cell-specific toxicities of a soluble or particulate compound that comprises contacting cultured human kidney and pulmonary cells with the compound at a range of concentrations, then labeling the cells with DNA, ?H2AX and actin markers and obtaining textural features, spatial correlation features, ratios of the markers, intensity features, cell count and morphology, estimating dose response curves and performing automatic classification of the compound using a random-forest algorithm.

Abstract: The present invention provides a device and method for removing non-discriminatory indices of an indexed dataset using ensemble statistics analysis. The device may include a data removal module (320) for removing non-discriminatory indices. For example, the data removal module (320) may comprise a common characteristic removal module and/or a noise removal module. In addition, the data analyzer (300) may comprise a normalization means (310) for normalizing the indexed data. The method of the present invention comprises the steps of identifying and removing portions of the set of data having insufficient discriminatory power based on ensemble statistics of the set of indexed data. For example, the method may include the steps of identifying and removing common characteristics and/or noise portions of the set of indexed data. In addition, the method may comprise the step of normalizing the indexed data either prior to or after the step of removing portions of the set of data.

Abstract: A multivariate, automated and scalable method for extracting profiles from images to quantify the effects of perturbations on biological samples. Morphological features are determined from images of treated (perturbed) and control (unperturbed) biological samples, and multivariate classification, for example, using a separating decision hyperplane, is used to separate the distribution of measured feature data into control and treated groups. This classification may be used to determine a magnitude of the effect of the particular perturbation under study. A practical application is high-throughput image-based drug screening, wherein the effects of many different compounds, each applied at different doses and for different exposure times, may be profiled to, for example, characterize compound activities and to identify dose-dependent multiphasic drug responses, or to determine and classify the biological effects of new compounds.

Abstract: The present invention provides a device and method for removing non-discriminatory indices of an indexed dataset using ensemble statistics analysis. The device may include a data removal module (320) for removing non-discriminatory indices. For example, the data removal module (320) may comprise a common characteristic removal module and/or a noise removal module. In addition, the data analyzer (300) may comprise a normalization means (310) for normalizing the indexed data. The method of the present invention comprises the steps of identifying and removing portions of the set of data having insufficient discriminatory power based on ensemble statistics of the set of indexed data. For example, the method may include the steps of identifying and removing common characteristics and/or noise portions of the set of indexed data. In addition, the method may comprise the step of normalizing the indexed data either prior to or after the step of removing portions of the set of data.