Deep Learning Bacterial Classification from Raw Genomic Sequences

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Traditional bacterial identification methods are time-consuming, labor-intensive, and have limited ability to classify novel or closely related strains, necessitating a need for faster, more accurate, and automated approaches in clinical diagnostics, environmental monitoring, and epidemiological investigations.

Innovation Solution

A deep learning-based system utilizing convolutional neural networks (CNNs) processes genomic sequences to classify bacteria by extracting features through multiple layers, correlating them to output classes, and employing ensemble network configurations for enhanced accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional bacterial identification methods (culture-based techniques, biochemical assays, BLAST, PCR, targeted sequencing) are used, then classification accuracy for known bacteria is maintained, but the process becomes time-consuming, labor-intensive, and resource-demanding

Engineering Contradiction:

Improveclassification speedVSAvoididentification time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces traditional mechanical and chemical bacterial identification methods (culture-based techniques, biochemical assays, PCR) with an automated computational system using deep learning algorithms. The system processes raw genomic sequences through preprocessing, feature extraction, and classification stages, eliminating manual laboratory operations and significantly reducing identification time while maintaining accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements preliminary action by pre-processing genomic sequences before classification, including extracting preserved regions (such as 16S rRNA genes), filtering, padding, and normalizing the data. This preparation work is done in advance to enable rapid classification without requiring time-consuming manual processing during actual identification

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If traditional bacterial identification methods are used, then established protocols are followed, but the ability to classify novel or closely related strains is limited

Engineering Contradiction:

Improveclassification capability for novel strainsVSAvoidclassification accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent changes the fundamental parameters of bacterial classification by using deep learning algorithms that can detect complex patterns and features in genomic sequences beyond traditional methods. The system transforms raw sequence data into classified outputs through multiple processing stages, enabling accurate classification of novel and closely related strains by learning from training data rather than relying on predefined protocols

Inventive Principle:

Principle #35Parameter changes

3Productivity

If deep learning algorithms are implemented for rapid bacterial classification, then processing speed and accuracy improve, but computational complexity and resource requirements increase

Engineering Contradiction:

Improveclassification speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the bacterial classification system into distinct functional modules: a preprocessing stage for data preparation, a feature extraction stage using convolutional neural networks, and a classification stage. This modular architecture manages computational complexity by organizing operations into separate, manageable components that can be processed sequentially

Inventive Principle:

Principle #1Segmentation

4Extent of automation

If deep learning algorithms are implemented for rapid bacterial classification, then automation level increases, but computational resource requirements increase

Engineering Contradiction:

Improveautomation levelVSAvoidcomputational resource consumption

Core Design Contradiction:

Extent of automationVSUse of energy by moving object

Solution Approach 1:

The patent extracts and focuses on specific preserved regions of genomic sequences (such as 16S rRNA genes) rather than processing entire genomes. This extraction approach reduces the volume of data requiring computational processing while maintaining classification accuracy, thereby reducing energy and computational resource consumption during automated analysis

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250299779A1Deep learning-based system for rapid and accurate bacterial classification

Publication Date: 2025.09.25 GALACTO CORP

AI summary

This disclosure is various methods and systems that utilize deep learning, specifically convolutional neural networks and recurrent neural networks to enable bacterial identification and classification by analyzing raw genomic sequences, such as the 16S rRNA gene and other preserved regions. The system involves multiple convolutional layers to extract and generalize features, correlate their presence, and ultimately classify the sequences into genera or species. RNNs, such as LSTMs, are used when the order of features matters, particularly in cases with padded regions or separators between gene segments.