Abstract: Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Abstract: A method of determining a clinical value for an individual based on a tumor in an image by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to determine a lymphocyte distribution of lymphocytes in the tumor based on the image; apply a classifier to the lymphocyte distribution to classify the tumor, the classifier having been trained to classify tumors into a class selected from at least two classes respectively associated with lymphocyte distributions; and determine the clinical value for the individual based on prognoses of individuals with tumors in the class into which the classifier classified the tumor.

Abstract: A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Abstract: This disclosure provides a technology for users to gain first-hand knowledge and experience with interpreting whole genomes. The technology graphically depicts variations in genome sequences in an expandable display, and provides a platform whereby the user may find and research the biological significance of such variants. The technology also provides a unique collaborative environment designed to capture and improve the collective knowledge of the participating community.

Abstract: Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Abstract: Various devices, systems, structures and methods are disclosed related to securely authorizing a transaction by synchronizing digital genomic data with associated synthetic genomic variants. An embodiment of the present invention utilizes digital genomic data associated with an entity, such as a person, who may utilize a genome-based security device to complete a transaction. In one embodiment, a person may use a genome-based security device to communicate with an external device over a wireless or other communication interface, synchronize digital genomic data and an associated synthetic variant received from the external device with digital genomic data and associated synthetic variant stored on the genome-based security device.

Abstract: Circulating free RNA (cfRNA) is used for monitoring status and/or treatment response for neural tumors, and especially glioma, glioblastoma, and neuroblastoma. Particularly preferred cfRNAs include those that encode a marker that is specific to a neural tumor, but also markers that are specific to DNA repair status and/or immune status.

Abstract: A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.

Abstract: The invention provides a method of validating the therapeutic composition that is prepared for immunotherapy of a tumor or cancer. The method includes, triggering of an immune response to a neoepitope of a subject's tumor by: a) obtaining neoepitope sequence data from the tumor of a subject; b) obtaining immune competent cells; c) using the neoepitope sequence data to generate a neoepitope presentation system; d) triggering an immune response by contacting the immune competent cells with the neoepitope presentation system; and e) quantifying the triggering of the immune response from the contacted immune competent cells.

Abstract: Methods for analyzing omics data and using the omics data to determine genetic distances and/or difference scores among a plurality of biological samples to so further determine the homogeneity of a group having a plurality of biological samples and/or exclude an individual biological sample from a group of biological samples as an outlier are presented. In preferred methods, a plurality of local differential string sets among the plurality of sequence strings is generated using a plurality of local alignments. The local different string is an indicative of genetic difference between one sequence string and one of the rests of the sequence strings among the plurality of sequence strings. From the plurality of local differential string sets, a plurality of difference scores among the plurality of sequence strings can be determined.

Abstract: Contemplated antiviral/cancer treatments comprise analysis of neoepitopes from viral DNA that has integrated into the host genome, and design of immunotherapeutic agents against such neoepitopes.

Abstract: Methods for analyzing omics data and using the omics data to determine genetic distances and/or difference scores among a plurality of biological uncles to so further determine the homogeneity of a group having a plurality of biological samples and/or exclude as individual biological sample from a group of biological samples a an outlier we presented. In preferred methods, a plurality of local differential string sea among do plurality of sequence strings is generated wing a plurality of local alignments. The local different suing is as indicative of genetic difference between one sequence string and one of do rests of do sequence strings among the plurality of sequence strings. From the plurality of local differential string sets, a plurality of difference scores among do plurality of sequence strings can be determined.

Abstract: Contemplated antiviral/cancer treatments comprise analysis of neoepitopes from viral DNA that has integrated into the host genome, and design of immunotherapeutic agents against such neoepitopes.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: The invention provides a method of validating the therapeutic composition that is prepared for immunotherapy of a tumor or cancer. The method includes, triggering of an immune response to a neoepitope of a subject's tumor by: a) obtaining neoepitope sequence data from the tumor of a subject; b) obtaining immune competent cells; c) using the neoepitope sequence data to generate a neoepitope presentation system; d) triggering an immune response by contacting the immune competent cells with the neoepitope presentation system; and e) quantifying the triggering of the immune response from the contacted immune competent cells.

Abstract: Various devices, systems, structures and methods are disclosed related to securely authorizing a transaction by synchronizing digital genomic data with associated synthetic genomic variants. An embodiment of the present invention utilizes digital genomic data associated with an entity, such as a person, who may utilize a genome-based security device to complete a transaction. In one embodiment, a person may use a genome-based security device to communicate with an external device over a wireless or other communication interface, synchronize digital genomic data and an associated synthetic variant received from the external device with digital genomic data and associated synthetic variant stored on the genome-based security device.

Abstract: Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

Abstract: Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.