Abstract: A system for classifying an origin of genomic sequence data may include a training database comprising sample data including first samples corresponding to sequences of natural origin and second samples corresponding to sequences of synthetic origin, and a classifier comprising a genomic classification module. The genomic classification module may be trained via machine learning on the first and second samples. The genomic classification module may be configured to receive genomic sequence data of any read length and determine a classification output indicating whether the genomic sequence data has a natural origin or synthetic origin.

Abstract: A system for classifying an origin of genomic sequence data may include a training database comprising sample data including first samples corresponding to sequences of natural origin and second samples corresponding to sequences of synthetic origin, and a classifier comprising a genomic classification module. The genomic classification module may be trained via machine learning on the first and second samples. The genomic classification module may be configured to receive genomic sequence data of any read length and determine a classification output indicating whether the genomic sequence data has a natural origin or synthetic origin.

Abstract: A learning health system and associated methods are provided. Biochemical assays are conducted at scheduled intervals on blood samples taken from an individual to provide, for each of a plurality of biochemical parameters, a time series of values representing the individual. Clinical parameters associated with the individual are extracted from a knowledge base. Genomic parameters are determined for the individual. An expected time series is calculated for each of a plural subset of the plurality of biochemical parameters from at least the clinical parameters and the genomic parameters. For each of the plural subset of biochemical parameters, the time series of values representing the individual is compared to the calculated expected time series to determine a likelihood of each of a plurality of conditions for the individual. The likelihood of at least one of the plurality of conditions is communicated to a user.

Abstract: A method for providing personalized healthcare for a patient is provided. The method may include receiving information indicative of a patient, retrieving a record associated with the patient, receiving a query identifying a healthcare related issue associated with the patient, performing analytics via a statistical discovery component and a natural language processing component configured to interface with respective portions of heterogeneous data sources to selectively identify correlations between genomic profile information of the patient and selected data of the data sources, applying a selected risk model based on the query; and providing a response to the query including information associated with clinical decision support tailored to an identity of the user, the user being a selected one of a patient, a researcher and a clinician.

Abstract: A learning health system and associated methods are provided. Biochemical assays are conducted at scheduled intervals on blood samples taken from an individual to provide, for each of a plurality of biochemical parameters, a time series of values representing the individual. Clinical parameters associated with the individual are extracted from a knowledge base. Genomic parameters are determined for the individual. An expected time series is calculated for each of a plural subset of the plurality of biochemical parameters from at least the clinical parameters and the genomic parameters. For each of the plural subset of biochemical parameters, the time series of values representing the individual is compared to the calculated expected time series to determine a likelihood of each of a plurality of conditions for the individual. The likelihood of at least one of the plurality of conditions is communicated to a user.