A method and system for annotation and classification of biomedical text having bacterial associations have been provided. The method is microbiome specific method for extraction of information from biomedical text which provides an improvement in accuracy of the reported bacterial associations. The present disclosure uses a unique set of domain features to accurately identify bacterial associations from the biomedical text. The disclosure further provides a method to use the set of domain features to improve a microbiome crowd sourcing setup and create a refined microbial association network. The refined bacterial association network can also be made corresponding to a disease or healthy state, which can be used for an improved understanding of the bacterial community structure and design therapeutic interventions. This refined bacterial association networks for a disease can then be used for clinical, therapeutic and diagnostic applications for treatment of the disease.

All the model-driven systems may not have capability to perform designing and execution of experiments, which limits functionality of such model-driven systems. The disclosure herein generally relates to Design of Experiments (DOE), and, more particularly, to a model driven sub-system for design and execution of experiments. The sub-system when plugged into the model driven system, uses legacy components as well components of the sub-system to perform designing and execution of the design of experiments.

All the model-driven systems may not have capability to perform designing and execution of experiments, which limits functionality of such model-driven systems. The disclosure herein generally relates to Design of Experiments (DOE), and, more particularly, to a model driven sub-system for design and execution of experiments. The sub-system when plugged into the model driven system, uses legacy components as well components of the sub-system to perform designing and execution of the design of experiments.

Systems, system integrations, non-transitory computer program products, and methods are described for managing digital data including therapeutic digital data or biological digital data. Such systems include at least one data processor and memory storing instructions, which when executed by at least one computing device result various operations. The digital data uploaded via a pre-defined pathway is received. The digital data is annotated with metadata based on a pre-defined annotation schema associated with the pre-defined pathway. The metadata facilitates storage and identification of the annotated digital data in a permanent data repository. Data encapsulating a notification of completion of the annotating is provided for further storage and analysis.

Systems, system integrations, non-transitory computer program products, and methods are described for managing digital data including therapeutic digital data or biological digital data. Such systems include at least one data processor and memory storing instructions, which when executed by at least one computing device result various operations. The digital data uploaded via a pre-defined pathway is received. The digital data is annotated with metadata based on a pre-defined annotation schema associated with the pre-defined pathway. The metadata facilitates storage and identification of the annotated digital data in a permanent data repository. Data encapsulating a notification of completion of the annotating is provided for further storage and analysis.

A method for processing sequencing data, including: (i) generating, by a sequencing platform, a plurality of sequencing signals from a sequencing operation, each of the plurality of sequencing signals representing a genetic sequence; (ii) sampling, by a controller, each of the plurality of sequencing signals at a Nyquist rate of the sequencing platform to generate an upsampled signal; (iii) receiving, for each of the plurality of sequencing signals, the respective upsampled signal and information associated with the respective sequencing signal, comprising a base pair read number and a time stamp for the respective sequencing signal; (iv) packaging, by the controller for each sequencing signal, the received upsampled signal, base pair read number, and time stamp into a data packet; (v) organizing the packaged data packets into a multiplexed single data stream; and (vi) transmitting the multiplexed single data stream to a remote system.

A method for processing sequencing data, including: (i) generating, by a sequencing platform, a plurality of sequencing signals from a sequencing operation, each of the plurality of sequencing signals representing a genetic sequence; (ii) sampling, by a controller, each of the plurality of sequencing signals at a Nyquist rate of the sequencing platform to generate an upsampled signal; (iii) receiving, for each of the plurality of sequencing signals, the respective upsampled signal and information associated with the respective sequencing signal, comprising a base pair read number and a time stamp for the respective sequencing signal; (iv) packaging, by the controller for each sequencing signal, the received upsampled signal, base pair read number, and time stamp into a data packet; (v) organizing the packaged data packets into a multiplexed single data stream; and (vi) transmitting the multiplexed single data stream to a remote system.

Systems and methods for processing sequence data are disclosed herein. In an embodiment, the system is comprised of a computing device that is configured for receiving, storing, and processing sequence data utilizing object-oriented functions. Sequencing is disclosed herein which provides for the customization of sequencing and analysis processing for next generation sequence processing and analysis. The system may be characterized as a bioinformatics system, which uses object oriented functions to process and store sequencing data efficiently and without the need for extensive programming knowledge. Object instances configured as part of the system may be manipulated, transformed, probed, and shared in memory, yet still saved to the disk. Due to the nature of sequence representation within the system, the required disk space needed is much less than existing bioinformatics programs. In another embodiment, MATLAB is utilized as part of the configuration of the system. Due to its object-oriented approach it may be adapted to more complex development functions and processing. This provides for much needed flexibility and ease of use.

There is provided a genome analysis device configured to analyze genome data including a large quantity of fragmented genome base sequences, and transmit output data concerning the genome data in response to an output request from a client device connected via a network, the genome analysis device including; a storage unit for storing data for visualization of multiple different layers, for the genome data; a request receiving unit for receiving an output request from the client device; and an output data generating unit for selecting data for visualization of a layer corresponding to the output request from the storage when the request receiving unit receives the output request, and generating output data based on the data for visualization of the selected layer.

The technology disclosed relates to a system for inter-model prediction score recalibration. The system includes a first model that generates, based on evolutionary conservation summary statistics of amino acids in a reference protein sequence, a first set of pathogenicity scores with rankings for variants that mutate the reference sequence to alternate protein sequences. The system further includes a second model that generates, based on epistasis expressed by amino acid patterns spanning a multiple sequence alignment aligning the reference sequence to non-target sequences, a second set of pathogenicity scores with rankings for the variants. The system further includes a rank loss determination logic that determines a rank loss parameter by comparing the two sets of rankings, a loss function reconfiguration logic that reconfigures a loss function based on the rank loss parameter, and a training logic that uses the reconfigured loss function to train the first model.