Synthetic Biological Data Generation via Machine Learning
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Solution Overview
Problem
Current methods for assessing biological aging rely heavily on physical biological samples, which may not always be feasible, and there is a need for technologies that can predict biological age without direct sampling, especially for interventions targeting individual-level aging processes.
Innovation Solution
A method involving machine learning platforms that receive real biological data signatures, generate input vectors, and produce synthetic biological data signatures, allowing for predictions based on attributes like age, sex, and tissue types, enabling simulations for both aging acceleration and rejuvenation without requiring physical samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical biological samples are used for assessing biological aging, then measurement precision is improved, but ease of operation deteriorates due to the need for invasive sampling procedures
Solution Approach 1:
The patent creates synthetic biological data signatures that copy the essential information from real biological samples without requiring physical sampling. The generative model learns the underlying distribution of biological aging markers and generates synthetic data that preserves the statistical properties and relationships found in real samples, enabling non-invasive biological age assessment
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the need for biological sample analysis and the desire for non-invasive assessment. The model is trained on real biological data and then generates synthetic signatures that mediate the assessment process, allowing predictions without direct sample contact
2Manufacturing precision
If real biological data signatures are collected from multiple subjects, then manufacturing precision of the aging model is improved, but loss of time increases due to extensive data collection requirements
Solution Approach 1:
The patent performs preliminary action by pre-training the generative model on a large dataset of real biological signatures before actual use. This upfront data collection and model training creates a robust foundation that can then generate accurate synthetic data without requiring time-consuming real-time sample collection for each assessment
Solution Approach 2:
The generative model becomes self-sufficient after training, generating its own synthetic biological data signatures without requiring continuous access to real biological samples. The model serves itself by internally generating the data needed for assessments, eliminating the need for ongoing external data collection
3Ease of operation
If synthetic biological data is generated using machine learning platforms, then ease of operation is improved by eliminating physical sampling, but reliability may deteriorate without direct biological validation
Solution Approach 1:
The patent implements feedback mechanisms where the generative model is trained on real biological data and its outputs are continuously refined by comparing generated synthetic signatures against actual sample data. This feedback loop ensures that the synthetic data maintains high fidelity to real biological patterns while enabling non-invasive operation
Solution Approach 2:
The patent creates a dynamic system where the generative model can adapt and refine its synthetic data generation based on the specific characteristics of the input query. The model dynamically adjusts the generated signatures to match the biological plausibility and statistical properties learned from training data, ensuring reliability while maintaining operational ease
Data Source
AI summary
Creating synthetic biological data for a subject can include: (a) receiving a real biological data signature derived from a biological sample of the subject; (b) creating input vectors based on the real biological data signature; (c) inputting the input vectors into a machine learning platform; (d) generating a predicted biological data signature of the subject based on the input vectors, wherein the predicted biological data signature includes synthetic biological data specific to the subject; and (e) preparing a report that includes the synthetic biological data of the subject. Biological pathway activation signatures can be genomics, transcriptomics, proteomics, metabolomics, lipidomics, glycomics, methylomics, or secretomics. Conditioning latent codes of the input vectors in a latent space of the machine learning platform with at least one constraint of an attribute of the subject is performed so the predicted biological data signature is based on the at least one constraint.