| CPC G16H 50/20 (2018.01) [G16H 50/30 (2018.01)] | 20 Claims |
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1. An apparatus for building a longevity profile, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive longevity measurement data of a user, wherein longevity measurement data comprises at least a transcriptomic datum;
generate at least a longevity hallmark as a function of the longevity measurement data utilizing a multiomics strategy by:
training a multi-label classifier to predict tumor subtypes using sample-wise pathway activity scores generated by single-sample gene enrichment analysis (ssGSEA) using a training dataset comprising correlations between tumor subtypes and treatments; and
generating the at least a longevity hallmark as a function of the longevity measurement data using the trained multi-label classifier;
wherein the at least a longevity hallmark comprises at least an aging trend, wherein the at least an aging trend comprises a plurality of correlations of endogenous metabolism activity to exogenous attributes;
generate longevity training data as a function of clinical assessment data and a longevity knowledge database, wherein the longevity training data comprises previously generated longevity hallmark data and longevity measurement data correlated to associated treatments, wherein the training data comprises previous outputs of a supervised longevity machine learning model;
train a supervised longevity machine learning model using the longevity training data;
generate a longevity profile of the user as a function of the trained supervised longevity machine learning model, wherein:
the trained supervised longevity machine learning model receives the at least a longevity hallmark and the longevity measurement as input and outputs the longevity profile, wherein the trained supervised longevity machine learning model is configured to correlate the at least a transcriptomic datum and the at least an aging trend to training data omics and simulated aging trends;
the trained supervised longevity machine learning model selects the simulated aging trend with a lowest aging trend for the correlation of the at least a transcriptomic datum and the at least an aging trend to training data omics and simulated aging trends; and
the longevity profile comprises a mosaic aging, wherein the mosaic aging comprises an idiosyncratic pattern related to aging that identifies weak spots in a user's body; and
transmit the longevity profile to an output device.
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