Genomic ML Models Using K-mer Extraction for Analysis Efficiency
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Solution Overview
Problem
Existing genomic predictive data analysis solutions face efficiency and reliability challenges due to the complexity and computational intensity of processing long genomic sequences, necessitating more efficient methods for feature extraction and prediction.
Innovation Solution
The use of frequency-based k-mer extraction layers and one-dimensional convolutional neural networks in machine learning models to identify frequent k-mers and viral replication origin k-mers, reducing the complexity of genomic predictive data analysis by focusing on defined-length subsequences, thereby improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to process long genomic sequences, then comprehensive genomic analysis can be performed, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides long genomic sequences into smaller k-mer segments (subsequences of length k) for processing. Instead of analyzing entire genomic sequences at once, the system processes these segmented k-mers individually, significantly reducing computational complexity while maintaining the ability to identify replication origins through frequency-based analysis of the segments
Solution Approach 2:
The patent extracts only the most frequent and relevant k-mers from genomic sequences using frequency-based extraction methods. By taking out and focusing only on the most significant k-mer patterns that are likely to indicate replication origins, the system reduces the data volume and computational requirements while preserving the essential information needed for accurate identification
2Reliability
If traditional methods are used to process long genomic sequences, then complete genomic information can be analyzed, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary frequency-based filtering of k-mers before detailed analysis. By pre-identifying and ranking k-mers based on their frequency of occurrence in the genomic sequences, the system prepares the data in advance, so that only the most promising candidates undergo computationally intensive analysis, thereby reducing overall processing time while maintaining reliability
Solution Approach 2:
The patent applies a partial action approach by focusing computational resources on analyzing only the most frequent k-mers rather than all possible k-mers. This selective analysis of a subset of high-frequency k-mers is sufficient to reliably identify replication origins without requiring exhaustive analysis of every possible sequence segment, thus reducing processing time while maintaining accuracy
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing genomic predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform genomic predictive data analysis operations by using at least one of viral genomic processing machine learning models and bacterial genomic processing machine learning models.