Abstract: A method of predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject can include: isolating a plurality of microorganism nucleic acids of microorganisms from a sample of a microbiota of the subject; analyzing the plurality of microorganism nucleic acids to determine an amount of the microorganisms of the microbiota based on the plurality of microorganism nucleic acids; generating a taxonomic profile of the microbiota of the subject based on the amount of each of the microorganisms; processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject; generating a report with the predicted phenotypical age of the subject; and providing the report to the subject.

Abstract: A method of creating a biological aging clock for a subject can include: (a) receiving a biological data signature derived from a tissue or organ of the subject; (b) creating input vectors based on the biological data signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological aging clock of the tissue or organ based on the input vectors by the machine learning platform, wherein the biological aging clock is specific to the tissue or organ; and (e) preparing a report that includes the biological aging clock that identifies a predicted biological age of the tissue or organ. The biological data signature can be based on biological pathway activation signatures for DNA methylomics.

Abstract: A method of predicting a phenotypical age of a subject based on a microflora taxonomic profile of a microbiota of the subject can include: isolating a plurality of microorganism nucleic acids of microorganisms from a sample of a microbiota of the subject; analyzing the plurality of microorganism nucleic acids to determine an amount of the microorganisms of the microbiota based on the plurality of microorganism nucleic acids; generating a taxonomic profile of the microbiota of the subject based on the amount of each of the microorganisms; processing the taxonomic profile of the microbiota with a computer configured with a machine learning platform in order to predict the phenotypical age of the subject; generating a report with the predicted phenotypical age of the subject; and providing the report to the subject.