Biological Aging Clock Using Transcriptomic Deep Learning
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Solution Overview
Problem
Current strategies for senescence reversal lack effective methods for rapid screening, validation, and clinical deployment, and there is a need for personalized treatments that can accurately predict the effects of drugs on human longevity and health span in a timely manner, as existing biomarkers are inadequate for measuring biological aging across multiple physiological systems.
Innovation Solution
The development of a method using machine learning and deep learning techniques to create a biological aging clock based on gene expression data from tissues or organs, allowing for the prediction of biological age and the selection of target genes or gene sets for anti-aging therapies, combined with a 5R strategy (Rescue, Remove, Replenish, Reinforce, Repeat) for treating senescence, which includes senolytic and senoremediation therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biomarkers (telomere length, protein aggregates, amino acid racemization) are used to measure aging, then measurement simplicity is maintained, but measurement precision and reliability are insufficient to capture biological aging across multiple physiological systems
Solution Approach 1:
The patent introduces gene expression profiles as an intermediary biomarker that bridges the gap between simple traditional markers and comprehensive physiological assessment. By measuring transcriptomic changes in response to standardized stressors, the system captures system-wide aging effects without requiring direct measurement of multiple organ systems, thus improving precision while managing complexity
Solution Approach 2:
The patent replaces complex mechanical/physical measurement systems (multiple biopsies, multiple organ assessments) with a biochemical assay system. A single tissue biopsy combined with transcriptomic analysis and computational modeling substitutes for numerous invasive procedures, achieving high measurement precision through molecular rather than mechanical means
2Reliability
If comprehensive strategies for repairing accumulated damage and activating endogenous repair processes are implemented, then reliability of senescence reversal is improved, but device complexity and treatment complexity increase significantly
Solution Approach 1:
The patent implements a feedback-driven treatment selection system where gene expression profiles are used to predict responsiveness to specific senescence-reversing compounds. This feedback mechanism allows the system to adapt treatment protocols to individual patients based on their molecular phenotype, improving reliability while reducing the need for trial-and-error approaches that would increase complexity
Solution Approach 2:
The patent performs preliminary screening and prediction of treatment responsiveness before initiating therapy. By using gene expression profiling to identify likely responders and predict effective compounds in advance, the system establishes a targeted treatment plan that increases reliability without requiring complex real-time adjustments during treatment
3Measurement precision
If multiple tissue biopsies are performed to assess aging in different organs, then measurement precision improves, but loss of time and procedural complexity increase
Solution Approach 1:
The patent uses gene expression profiles as an intermediary that reflects system-wide aging states. Instead of directly measuring multiple organs, the transcriptomic response to standardized stressors serves as a proxy that integrates information about the functional state of multiple physiological systems, achieving multi-organ assessment precision through a single tissue sample
Solution Approach 2:
The patent develops a universal biomarker system where gene expression profiles from a single tissue type can assess aging across multiple organ systems. The transcriptomic response to standardized stressors provides universal information about systemic aging that applies to multiple organs simultaneously, eliminating the need for organ-specific biopsies
4Reliability
If personalized treatments based on deep neural networks and gene expression profiles are developed, then treatment effectiveness improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent implements a self-service system where the computational model automatically processes gene expression data and generates treatment predictions without requiring manual interpretation. The deep neural network performs self-learning and adaptation, reducing the need for complex human-in-the-loop decision support systems while maintaining high treatment effectiveness
Solution Approach 2:
The patent transforms complex gene expression data into simplified predictive parameters that can be processed by the computational model. By converting high-dimensional transcriptomic data into meaningful aging scores and treatment response predictions, the system reduces computational complexity while preserving the information needed for effective personalized treatment selection
Data Source
AI summary
A method of creating a biological aging clock for a subject can include: (a) receiving a transcriptome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the transcriptome 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.