Variance Analysis Engine for Genomic Label Selection
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Solution Overview
Problem
Existing methods for predicting antibiotic drug susceptibility from genomic datasets often lead to overfitting and inaccurate classifications due to the inclusion of non-significant labels, which consume processing power and result in poor predictions.
Innovation Solution
The implementation of a variance analysis engine to assess the effect size of labels in genomic datasets, selecting labels with high effect sizes for use in a machine learning model, such as a random forest model, to improve prediction accuracy by prioritizing significant features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are applied to large genomic datasets with multiple labels, then prediction capability is improved, but processing power consumption increases and overfitting occurs
Solution Approach 1:
The patent extracts and removes non-significant labels from the genomic dataset before applying machine learning. The variance analysis engine calculates effect sizes for each label and identifies which labels contribute meaningfully to prediction. By taking out only the significant labels (those exceeding a threshold effect size), the system reduces processing power consumption while maintaining prediction accuracy, avoiding the need to process all labels in the large dataset.
Solution Approach 2:
The patent segments the label set into significant and non-significant groups based on effect size calculations. The variance analysis engine divides the original large dataset into manageable components by identifying subsets of labels that truly contribute to prediction. This segmentation allows the machine learning model to focus only on relevant segments, reducing computational burden while preserving predictive capability.
2Reliability
If all labels from genomic datasets are included in the machine learning model, then comprehensive analysis is achieved, but classification accuracy deteriorates due to non-significant labels
Solution Approach 1:
The patent extracts only the significant labels from the comprehensive genomic dataset using variance analysis. The effect size calculation identifies which labels have meaningful relationships with the target variable, and non-significant labels are removed before model training. This extraction process simplifies the model while improving classification accuracy by eliminating noise from irrelevant labels.
Solution Approach 2:
The patent changes the parameter of label significance by introducing effect size as a filtering criterion. Instead of using all labels equally, the system transforms the label set by applying a threshold based on calculated effect sizes. This parameter change allows the model to focus on labels with sufficient statistical significance, improving classification accuracy while reducing model complexity.
3Reliability
If non-significant labels are included in the analysis, then complete data utilization is achieved, but prediction reliability decreases due to overfitting
Solution Approach 1:
The patent extracts and removes non-significant labels that would cause overfitting. The variance analysis engine identifies labels with effect sizes below a threshold, indicating they do not contribute meaningfully to prediction. By taking out these non-significant labels, the system prevents overfitting and improves prediction reliability, while the removal of noisy data does not result in loss of valuable predictive information.
Data Source
AI summary
Methods and systems for predicting the susceptibility of bacterial pathogens to antibiotics using genomic data sets. Various embodiments described herein receive a genomic dataset and a set of labels and run principal variance component analysis thereon to determine the effect sizes of the labels. One or more labels are then selected based on their effect sizes and used in a machine learning model to make predictions on future datasets.