Gene Regulatory Sequence Selection for Early Endophenotype Prediction
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Solution Overview
Problem
Existing methods for manipulating plant biological processes to achieve desired phenotypes are time- and space-inefficient, as they rely on waiting for phenotypes to develop in mature plants, while intermediate endophenotype biomarkers at smaller scales are often overlooked.
Innovation Solution
A method using machine-learning models to predict and modify endophenotypes in plants by analyzing gene regulatory sequences, involving training models with self-supervised predictions, fine-tuning, and utilizing sequence space-sampling algorithms to select and introduce desired gene regulatory sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods of waiting for phenotypes to develop in mature plants are used, then accurate phenotype observation is achieved, but the process is time- and space-inefficient
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict endophenotypes at the molecular level (gene expression, protein abundance) before the actual phenotype manifests in mature plants. This allows researchers to identify and select desired traits early in the plant lifecycle, avoiding the need to wait for full phenotypic development.
Solution Approach 2:
The patent introduces intermediate endophenotype biomarkers as mediators between genetic modification and final phenotype observation. These molecular-level intermediates (mRNA expression levels, protein abundance) serve as proxies that can be measured early and predict the ultimate phenotype, bridging the gap between genotype and phenotype without requiring mature plant development.
2Productivity
If machine-learning models are used to predict endophenotypes from gene regulatory sequences, then prediction speed is improved, but model training complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of gene regulatory sequences and corresponding endophenotype measurements before actual prediction tasks. This pre-training phase, though computationally intensive, is performed once and enables rapid predictions thereafter, resolving the contradiction between initial complexity and subsequent speed.
Solution Approach 2:
The patent employs self-supervised learning approaches where the model learns to predict endophenotypes from gene regulatory sequences using its own predictions and corrections. The system automatically refines its internal representations through iterative training on unlabeled data, reducing the need for extensive manual annotation and simplifying the overall training process.
Data Source
AI summary
A method for generating a gene regulatory sequence with a desired endophenotype profile includes obtaining a plurality of gene regulatory sequences and inputting the plurality of gene regulatory sequences into a machine-learning model trained to obtain a plurality of effect predictions corresponding to a plurality of endophenotypes. The method further includes selecting one or more desired endophenotypes based on the plurality of endophenotypes and selecting a gene regulatory sequence in accordance with the one or more desired endophenotypes.