Incremental Learning Framework for Genomic Classification
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Solution Overview
Problem
The rapid growth of genomic data outpaces computational processing capabilities, leading to increased computational intensity in updating taxonomic classification models, particularly in metagenomics, where new species and strains are frequently discovered, causing inefficiencies in data access and processing.
Innovation Solution
An incremental learning framework and software system that uses advanced machine-learning techniques to automatically update classification models, create new clusters, and assign pseudo-labels, reducing the time required for classification and improving performance with limited computational resources by leveraging both automated and manual data ingestion and linking operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the whole database is reprocessed to update the classifier when new genomes are added, then the classification accuracy is improved, but the computational time and resources increase significantly
Solution Approach 1:
The patent divides the database into existing data and new data segments. Instead of reprocessing the entire database, the system processes only the new genome segments and integrates them with existing classification results, significantly reducing computational time while maintaining accuracy
Solution Approach 2:
The system performs preliminary processing of new genome data before full integration. By pre-processing and preparing new data in advance, the system reduces the overall update time and computational burden when integrating new genomes into the existing classification model
2Measurement precision
If the whole database is reprocessed to update the classifier when new genomes are added, then the classification completeness is improved, but the computational resources increase significantly
Solution Approach 1:
The patent extracts only the necessary new genome data from the updated database for processing, rather than reprocessing the entire database. This extraction approach ensures classification completeness is maintained while significantly reducing computational resource consumption
Solution Approach 2:
The system segments the processing task to handle only new genome additions separately from existing data. This segmentation allows the system to maintain complete classification coverage while using fewer computational resources by avoiding redundant processing of unchanged data
3Adaptability or versatility
If frequent updates of classification results are provided, then the system adaptability is improved, but the processing time for each update increases
Solution Approach 1:
The patent implements a dynamic update mechanism that adjusts processing based on the amount and type of new data. The system can perform quick incremental updates for small additions while maintaining the ability to do comprehensive updates when necessary, achieving both frequent updates and reasonable processing times
Solution Approach 2:
The system performs preliminary assessments and preprocessing of new genome data before full integration. This preliminary action enables faster update cycles while maintaining adaptability, as the pre-processing step prepares data for quick incorporation into the existing classification model
Data Source
AI summary
An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.