Multi-Model Feature Selection for Large-Scale Omics Ranking
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Solution Overview
Problem
Conventional methods for feature selection in omics data analysis are inefficient and resource-intensive when dealing with large volumes of omics features, often requiring extensive computational resources and time due to the inclusion of multiple types of omics data without effective filtering.
Innovation Solution
A feature selection method utilizing multiple training models to select features based on statistical indices, feature extraction operations, and classification into groups, followed by multi-level selection and validation using multiomics pathway analysis to identify the most influential features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all omics features are selected for analysis, then the comprehensiveness of the analysis is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent segments the feature selection process into multiple stages: initial feature selection using machine learning models to identify candidate features, followed by secondary filtering using statistical tests and domain knowledge. This multi-stage segmentation allows comprehensive analysis to be performed systematically without requiring all features to be processed simultaneously, thereby reducing overall computational time while maintaining analysis comprehensiveness.
Solution Approach 2:
The patent applies preliminary action by pre-filtering omics features using statistical significance tests and domain-specific criteria before applying computationally intensive machine learning models. This preliminary filtering reduces the feature space that requires full machine learning analysis, significantly reducing computational time while ensuring that only potentially relevant features undergo comprehensive modeling.
2Reliability
If all omics features are selected for analysis, then the completeness of the data is improved, but the computational resources required increase significantly
Solution Approach 1:
The patent extracts only the most relevant omics features through a multi-layered filtering process that combines statistical significance testing, machine learning-based feature selection, and domain knowledge validation. By extracting only the essential features that meet multiple selection criteria, the system maintains data completeness for relevant features while eliminating computationally expensive processing of irrelevant features, thereby reducing overall computational resource consumption.
Solution Approach 2:
The patent applies partial action by performing comprehensive analysis only on features that pass through multiple filtering stages, rather than analyzing all features equally. Features that fail to meet selection criteria at any stage are excluded from further computationally intensive processing, allowing the system to achieve high-quality results for selected features while consuming fewer computational resources overall.
3Measurement precision
If multiple types of omics data are combined, then the accuracy of disease prediction is improved, but the complexity of feature selection increases
Solution Approach 1:
The patent segments the multi-omics feature selection process by applying different selection strategies and criteria to different omics data types (e.g., transcriptomics, proteomics, metabolomics). Each omics layer undergoes tailored filtering and selection appropriate to its characteristics, followed by integration of selected features. This segmentation reduces overall complexity by avoiding a monolithic approach while maintaining comprehensive multi-omics analysis for improved prediction accuracy.
Solution Approach 2:
The patent applies local quality by implementing feature selection criteria and methods that are optimized for each specific omics data type rather than using a uniform approach. Different statistical tests, weighting schemes, and selection thresholds are applied locally to each omics layer based on its data characteristics, enabling accurate handling of multi-omics complexity while improving the overall quality of feature selection for disease prediction.
Data Source
AI summary
A feature selection method is provided, including: inputting a plurality of pieces of training data into a plurality of training models to perform selection in a plurality of features through each training model for obtaining multiple feature pools; sorting the features based on the number of times each feature is selected by the feature pools to obtain a feature ranking; and extracting a plurality of designated features from the features based on the feature ranking.