A method for visualizing microbiome data is described. Respective microbes and/or genes in microbiome data stored In in a database are identified. A network comprising nodes interconnected by edges is generated in a memory of a computer, each node representing one or more identified microbes or one or more microbial metabolites, and each edge of the network representing an association between a respective pair of the one or more identified microbes or a reaction mediated between two metabolites by an enzyme encoded in the one or more identified genes, with at least some nodes and edges of the network being each associated with a condition attribute identifying a groups and/or a timestamp associated with a sample in the database. The displayed network is dynamically updated in accordance with a filtering of the microbiome data based on the condition attributed and/or the timestamp attributed. Corresponding systems and computer-readable storages are also described.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.

Methods, apparatus, system and computer-implemented method are provided for a computer-implemented method of identifying candidate entities of interest associated with disease selection information. The method including: receiving a first set of entities that are predicted to be associated with the disease selection information; retrieving a second set of entities that are known to be associated with the disease selection information; generating a set of entity mappings between entities of the first set of entities, entities the second set of entities, and entities of a graph structure in relation to the disease selection information, the graph structure based on an entity hierarchy, ontology or taxonomy of an entity family associated with the first and second sets of entities, linking entities from the first and second sets of entities to the graph structure based on the generated set of entity mappings; and identifying candidate entities of interest from those linked entities of the first and second sets of entities on the graph structure based on determining where each entity from the first set of entities is located on the graph structure relative to one or more entities of the second set of entities on the graph structure.

Methods, apparatus, system and computer-implemented method are provided for a computer-implemented method of identifying candidate entities of interest associated with disease selection information. The method including: receiving a first set of entities that are predicted to be associated with the disease selection information; retrieving a second set of entities that are known to be associated with the disease selection information; generating a set of entity mappings between entities of the first set of entities, entities the second set of entities, and entities of a graph structure in relation to the disease selection information, the graph structure based on an entity hierarchy, ontology or taxonomy of an entity family associated with the first and second sets of entities, linking entities from the first and second sets of entities to the graph structure based on the generated set of entity mappings; and identifying candidate entities of interest from those linked entities of the first and second sets of entities on the graph structure based on determining where each entity from the first set of entities is located on the graph structure relative to one or more entities of the second set of entities on the graph structure.

Systems and methods of storing and retrieving K-mer data in a data structure are provided. In certain embodiments, the K-mer data is stored as an integer value that defines an address of a slot in the data structure. In many embodiments, each slot in the data structure stores the remaining portion of the K-mer that is not part of the prefix. Additional embodiments are directed to genetic or genomic analysis using a data structure for storing K-mer data.

Systems and methods of storing and retrieving K-mer data in a data structure are provided. In certain embodiments, the K-mer data is stored as an integer value that defines an address of a slot in the data structure. In many embodiments, each slot in the data structure stores the remaining portion of the K-mer that is not part of the prefix. Additional embodiments are directed to genetic or genomic analysis using a data structure for storing K-mer data.

An image generation device includes an imaging unit configured to convert data representing an expression level for each microRNA type into image-rendition data serving as data representing a matrix of two dimensions or more, a classification unit configured to perform classification of the image-rendition data, and a contribution-presentation-image generation unit configured to generate a contribution-presentation image representing a contribution of a specific part of the image-rendition data to the classification.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient's sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.