Abstract: A method performed by a computer system including one or more electronic processors and memory includes receiving genomic data, ocular phenotypic data, and receiving ocular clinical diagnosis data for a plurality of subjects. The method also includes identifying a mapping function that maps the genomic data for the plurality of subjects to the ocular clinical diagnosis data for the plurality of subjects and a mapping function that maps the ocular phenotypic data for the plurality of subjects to the ocular clinical diagnosis data for the plurality of subjects. The method may also include obtaining one or more scores indicative of clinical states of an ocular disease for a respective subject of the plurality of subjects. The method may further include treating the plurality of subjects based on the one or more scores.

Abstract: Predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is described. A training data set is constructed from a series of libraries, including at least one library comprising genomic, proteomic, and research data specific to non-humans. A predictive machine learning model is developed based on the constructed training data set and utilized to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. A subset of sequences is selected for evaluation from the plurality of sequences based on the predicted quality or performance and candidate samples are designed derived from the non-human donor using the selected subset of sequences.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: A method for predictive engineering of a sample derived from a genetically optimized non-human donor suitable for xenotransplantation into a human having improved quality or performance is provided. The method includes constructing a training data set from a series of libraries, wherein at least one library in the series of libraries comprises genomic, proteomic, and research data specific to non-humans. The method includes developing a predictive machine learning model based on the constructed training data set. The method includes utilizing the predictive machine learning model to obtain a predicted quality or performance of a plurality of sequences for a candidate sample from the non-human donor specific to a human patient or patient population. The method includes selecting a subset of sequences for evaluation from the plurality of sequences based on the predicted quality or performance.

Abstract: The specification relates to a self-pipelining workflow management system. The system can receive a request to run a bioinformatics analysis and automatically create a workflow by accessing a knowledge structure. The knowledge structure can include a plurality of predicates describing computational relationships between at least one bioinformatics data file and at least two bioinformatics programs. The workflow contains a dynamic set of predicates specific to the request based upon initial input data, general request parameters and the knowledge structure. The workflow is initiated based on a first predicate of the dynamic set of predicates and after a new unprocessed input data is obtained, the dynamic set of predicates is updated. The workflow continues until no more predicates can be associated with the unprocessed input data or no more unprocessed data can be obtained.