Machine Learning for Plant Nitrogen Use Efficiency Prediction
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Solution Overview
Problem
Current methods face challenges in accurately predicting complex phenotypic traits from genome-scale information due to data sparsity, multicollinearity, and overfitting, particularly in predicting nitrogen use efficiency (NUE) in plants, which is crucial for crop improvement and sustainability.
Innovation Solution
An evolutionarily informed machine learning approach that utilizes transcriptome data of nitrogen response genes, conserved across species like maize and Arabidopsis, to identify key genes for improving NUE, involving dimension reduction and validation using mutants to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models use genome-scale information to predict nitrogen use efficiency, then prediction capability is improved, but data sparsity and overfitting problems worsen
Solution Approach 1:
The patent combines transcriptome data from multiple species (maize and Arabidopsis) to create a cross-species predictive model. By merging data across species boundaries and using evolutionarily conserved genes as features, the model overcomes data sparsity in individual species while maintaining prediction accuracy for nitrogen use efficiency
Solution Approach 2:
The patent extracts and selects only the most relevant features (evolutionarily conserved genes) from the genome-scale data. This feature selection process removes redundant and irrelevant information, reducing dimensionality and preventing overfitting while retaining the predictive power needed for accurate NUE prediction
2Measurement precision
If machine learning models use genome-scale information to predict nitrogen use efficiency, then prediction capability is improved, but model complexity and computational burden worsen
Solution Approach 1:
The patent extracts and selects only the most relevant features (evolutionarily conserved genes) from the genome-scale data. This feature selection process removes redundant and irrelevant information, reducing dimensionality and preventing overfitting while retaining the predictive power needed for accurate NUE prediction
Solution Approach 2:
The patent segments the complex genome-scale data into manageable components by focusing on specific functional categories (evolutionarily conserved genes). This segmentation allows the model to process information in organized modules rather than as an overwhelming monolithic dataset
3Measurement precision
If transcriptome data from multiple species is used, then prediction accuracy is improved, but experimental complexity and validation difficulty worsen
Solution Approach 1:
The patent uses evolutionarily conserved genes as universal features that function across multiple species. These conserved genetic elements serve as a common language between maize and Arabidopsis, allowing the model to transfer knowledge across species boundaries while maintaining biological relevance and reducing the need for species-specific validation experiments
Data Source
AI summary
Provided are machine learning methods for identifying genes that affect plant properties. Also provided are plant cell sand plants comprising genetic modifications that improve plant nitrogen utilization and increased biomass. Methods of making the modified plant cells and plants are also provided.