Genetic Feature Segmentation for ML Model Training
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Solution Overview
Problem
Existing machine learning models face challenges in effectively utilizing genetic data due to the large number of input variables, sparse data, and the difficulty in identifying relevant features for classification tasks.
Innovation Solution
The development of systems and methods for generating machine learning models using genetic data, which involves identifying and using input features such as aligned and non-aligned variables to train models, thereby enabling accurate classification of biological samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If genetic data with large number of variables is used as input to machine learning models, then the potential information available for classification is increased, but the difficulty of training the model and identifying relevant features increases
Solution Approach 1:
The patent segments the large set of genetic variables into smaller, manageable groups or features that are more relevant to the classification task. This segmentation reduces the complexity of training by organizing the voluminous genetic data into structured, meaningful units that can be processed more efficiently by machine learning models.
Solution Approach 2:
The patent extracts and selects specific relevant features from the large set of genetic variables. By identifying and extracting only the most pertinent variables for the classification task, the model training process becomes more manageable while still capturing the essential information needed for accurate classification.
2Measurement precision
If the number of features available for inference is increased, then the potential accuracy of classification is improved, but the number of samples required to train the model effectively increases
Solution Approach 1:
The patent applies local quality by focusing on specific regions or aspects of the genetic data that are most relevant to the classification task. Rather than uniformly processing all features, the method identifies and emphasizes locally relevant features, thereby achieving high classification accuracy with fewer samples.
Solution Approach 2:
The patent performs preliminary feature selection and data preprocessing before the main classification task. This preliminary action involves preparing and organizing the genetic data in advance, identifying relevant features, and structuring the input data, which reduces the number of samples needed during actual model training while maintaining high accuracy.
3Reliability
If more input features are used to improve classification performance, then the model's ability to capture genetic patterns is enhanced, but the computational resources and time required for training increase
Solution Approach 1:
The patent applies partial action by using a subset of the most relevant features rather than all available genetic variables. This selective approach captures sufficient genetic patterns for accurate classification while significantly reducing the computational burden and training time required.
Solution Approach 2:
The patent changes parameters by optimizing feature selection criteria and model configuration to achieve better performance-to-time ratios. By adjusting parameters such as feature importance thresholds, dimensionality reduction techniques, and model complexity settings, the method maintains high classification performance while minimizing training time.
Data Source
AI summary
Systems, methods, and apparatuses for generating and using machine learning models using genetic data. A set of input features for training the machine learning model can be identified and used to train the model based on training samples, e.g., for which one or more labels are known. As examples, the input features can include aligned variables (e.g., derived from sequences aligned to a population level or individual references) and/or non-aligned variables (e.g., sequence content). The features can be classified into different groups based on the underlying genetic data or intermediate values resulting from a processing of the underlying genetic data. Features can be selected from a feature space for creating a feature vector for training a model. The selection and creation of feature vectors can be performed iteratively to train many models as part of a search for optimal features and an optimal model.