Abstract: We propose a method of generating an epigenetic age prediction. The method includes providing a sample extraction test kit, and receiving a sample extracted by the sample extraction test kit. The method further includes extracting DNA from the sample and processing the DNA to receive processed DNA. The method further includes amplifying a plurality of loci in the processed DNA to receive amplified DNA and processing the amplified DNA to receive a plurality of methylation values for one or more CpG sites in the plurality of loci.

Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.

Abstract: We propose a method of generating an epigenetic age prediction. The method includes providing a sample extraction test kit, and receiving a sample extracted by the sample extraction test kit. The method further includes extracting DNA from the sample and processing the DNA to receive processed DNA. The method further includes amplifying a plurality of loci in the processed DNA to receive amplified DNA and processing the amplified DNA to receive a plurality of methylation values for one or more CpG sites in the plurality of loci.

Abstract: We propose an epigenetic age predictor and a method of training the same. The epigenetic age predictor is configured to receive a plurality of inputs corresponding to methylation values at CpG sites. The epigenetic age predictor predicts an epigenetic age of an individual based on the plurality of inputs.

Abstract: We propose an epigenetic age predictor and a method of training the same. The epigenetic age predictor is configured to receive a plurality of inputs corresponding to methylation values at CpG sites. The epigenetic age predictor is configured to receive a plurality of inputs corresponding to phenotypic values of an individual. The epigenetic age predictor predicts an epigenetic age of the individual based on the sequence of inputs.

Abstract: The technology disclosed relates to a system and method of conducting virtual clinical trials. The system comprises a sponsor server configured to specify a target mapping of a clinical trial objective mapper. The target mapping maps participant-specific clinical data to an objective of a virtual clinical trial. The system comprises a plurality of edge devices accessible by respective participants in a plurality of participants. The system comprises a clinical trial conductor server configured to distribute coefficients of the clinical trial objective mapper to respective edge devices to implement distributed training of the clinical trial objective mapper. The clinical trial conductor server is configured to receive participant-specific gradients generated during the distributed training in response to processing participant-specific clinical data.

Abstract: The technology disclosed relates to efficient tertiary analysis of genomic data. The technology disclosed includes splitting a genomic data file into a plurality of segments, and storing segments in the plurality of segments across nodes of a distributed storage system, pushing the segments from the nodes of the distributed storage system to nodes of a distributed, in-memory computing engine, distributing directives of tertiary analysis job contexts for the genomic data file across the nodes of the distributed, in-memory computing engine, directly executing the distributed directives on the segments stored on the nodes of the distributed, in-memory computing engine to cause parallel processing of the segments, and aggregating results of the parallel processing across the nodes of the distributed, in-memory computing engine to produce an output.