Deep Learning Algorithms for Embryonic-Fetal Transition Detection
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Solution Overview
Problem
Current methods for modulating tissue regeneration in mammals are limited by the lack of effective markers and molecular regulators for the embryonic-fetal transition (EFT), which hinders research and therapeutic applications in regenerative medicine and cancer treatment.
Innovation Solution
Development of deep learning algorithms and methods to identify and modulate the embryonic-fetal transition (EFT) in mammalian cells, using RNA expression profiles and regulatory noncoding RNAs and mRNAs, to induce tissue regeneration and cancer cell maturation, and to screen for agents capable of modulating these pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms and RNA expression profiling methods are used to identify and modulate the embryonic-fetal transition, then the precision of detecting cellular phenotypes and modulating EFT pathways is improved, but the complexity of the detection and measurement process increases
Solution Approach 1:
The patent uses deep learning algorithms as an intermediary tool to analyze RNA expression profiles and identify the embryonic-fetal transition. The algorithm acts as a mediator between raw RNA sequencing data and the detection of cellular phenotype changes, automatically identifying patterns and transitions without requiring manual analysis of complex gene expression data.
Solution Approach 2:
The patent replaces traditional manual or simple computational methods for analyzing RNA expression data with deep learning algorithms. This substitution of mechanical/manual analysis with automated computational intelligence reduces the difficulty of detecting and measuring EFT pathways while improving measurement precision through advanced pattern recognition.
2Reliability
If markers and molecular regulators for the embryonic-fetal transition are identified and used, then the effectiveness of modulating tissue regeneration is improved, but the complexity of the system increases
Solution Approach 1:
The patent extracts specific markers and molecular regulators (such as LIN28A, LIN28B, and associated RNA molecules) that control the embryonic-fetal transition from the complex biological system. By identifying and isolating these key regulatory elements, the patent enables targeted modulation of tissue regeneration without needing to manipulate the entire complex biological network.
Solution Approach 2:
The patent applies local quality by focusing on specific molecular regulators and markers rather than attempting to control the entire embryonic-fetal transition system. By targeting specific genes and RNA molecules that locally control the transition, the patent achieves effective tissue regeneration modulation while managing system complexity through focused intervention points.
Data Source
AI summary
Aspects of the present invention include algorithms, methods and compositions related to the modulation of molecules regulating the mammalian transition from embryonic to fetal development. Methods and compositions for the use of such modulations to increase the regenerative potential in fetal and adult tissues otherwise incapable of scarless regeneration are also presented.