DEEP LEARNING-BASED SYSTEM FOR RAPID AND ACCURATE BACTERIAL CLASSIFICATION

Smart overview of the Invention

The patent application outlines a system leveraging deep learning for bacterial classification. It employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze genomic sequences, notably the 16S rRNA gene. The system utilizes multiple convolutional layers to extract features from these sequences and classifies them into specific genera or species. RNNs, particularly Long Short-Term Memory networks (LSTMs), handle cases where the sequence order is crucial, such as when dealing with padded regions or separators.

Technical Field

This invention intersects microbiology, bioinformatics, and machine learning. It provides a method for automated bacterial identification using genomic sequence data processed through deep learning algorithms. The system aims to improve upon traditional methods by offering a faster, more accurate solution for identifying bacteria in various fields including clinical diagnostics and environmental monitoring.

Background

Traditional bacterial identification methods are often slow and labor-intensive, relying on culture-based techniques, biochemical assays, and molecular methods like PCR. These conventional approaches can struggle with novel or closely related strains. There is a pressing need for more efficient and automated systems capable of rapid identification, especially in scenarios like pathogen detection or disaster response.

System Components

The classifier system consists of a processing component, memory storing executable instructions, and a classifier CNN. This setup processes preserved genomic regions by filtering, padding, and aggregating sequences before feeding them into the neural network. The network classifies these sequences into known genera and species using an ensemble network configuration. The method involves extracting genomic regions, preprocessing them, organizing data files with genus and species labels, and training classification models.

Illustrations

The accompanying drawings provide visual representations of the system's implementation. They include schematics of the classification system, user interface screenshots showing inputs and outputs, model architecture with convolutional layers, and examples of genomic sequence encoding. Flowcharts illustrate model training and prediction processes, highlighting the detailed steps in the system's operation.