Biological Aging Clock Using Deep Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current strategies for senescence reversal lack methods for rapid screening, validation, and clinical deployment, and there is a need for timely prediction of drug effects on human longevity and health span, with existing biomarkers being inadequate for accurately measuring biological aging across multiple physiological systems.
Innovation Solution
The development of a method using deep neural networks and machine learning to analyze transcriptomic and proteomic data for predicting biological age, allowing for personalized senescence therapies and the identification of targets for anti-aging treatments by generating biological aging clocks specific to tissues or organs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks and machine learning are used to analyze transcriptomic and proteomic data for predicting biological age, then measurement precision of biological aging is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex biological aging prediction task into distinct computational modules: transcriptomic data processing, proteomic data processing, and integrated machine learning analysis. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces computational intermediaries including trained neural network models and pre-processed feature sets that mediate between raw transcriptomic/proteomic data and final biological age predictions. These intermediaries simplify the complexity by encapsulating complex relationships in reusable computational components.
2Measurement precision
If comprehensive transcriptomic and proteomic analysis is performed to accurately measure biological aging across multiple physiological systems, then measurement precision is improved, but loss of time for data processing and analysis increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive transcriptomic and proteomic datasets before actual biological age prediction. This pre-processing creates ready-to-use computational models that can rapidly predict biological age without requiring real-time comprehensive analysis of all data during actual use.
Solution Approach 2:
The patent transforms comprehensive transcriptomic and proteomic data into simplified parameter representations that capture essential aging information. By changing the parameters from raw data to processed features, the system maintains measurement precision while reducing processing time for subsequent analysis.
3Adaptability or versatility
If tissue-specific gene expression and protein production profiles are analyzed to develop personalized senescence reversal treatments, then adaptability of treatment is improved, but device complexity and analysis requirements increase
Solution Approach 1:
The patent applies local quality by analyzing tissue-specific gene expression and protein production profiles rather than treating all tissues uniformly. This allows the system to adapt treatments to the specific characteristics of each tissue, improving personalization while managing complexity through focused local analysis rather than comprehensive global analysis.
Data Source
AI summary
A method of creating a biological aging clock for a subject can include: (a) receiving a proteome signature derived from a tissue or organ of the subject; (b) creating input vectors based on the proteome 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.