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Updated: Dec 17, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Ruopeng Xie¹, Jiahui Li¹, Jiawei Wang²

¹Bioinformatics Lab at Guilin University of Electronic Technology.

Briefings in Bioinformatics

|June 30, 2020

Summary

DeepVF is a novel deep learning framework that accurately predicts bacterial virulence factors (VFs) using an enlarged dataset and diverse features. This tool aids in identifying potential VFs from bacterial genomes.

Keywords:

deep learning ensemble learning machine learning recognition sequence analysis virulence factor prediction

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Virulence factors (VFs) are crucial for pathogen infection.
Existing VF prediction tools face limitations due to evolving pathogen characteristics and limited feature engineering.

Purpose of the Study:

To develop an improved computational framework for accurate prediction of bacterial virulence factors.
To address limitations of existing methods by utilizing updated datasets and comprehensive feature exploration.

Main Methods:

Developed DeepVF, a deep learning-based hybrid framework using a stacking strategy.
Employed an enlarged, up-to-date dataset and explored heterogeneous features.
Trained 62 baseline models using classical and deep learning algorithms, integrated via stacking.

Main Results:

DeepVF demonstrated more accurate and stable performance compared to baseline models.
The framework significantly outperformed state-of-the-art VF predictors on an independent test set.
An online user-friendly predictor for DeepVF was implemented.

Conclusions:

DeepVF offers a powerful and effective approach for identifying virulence factors from bacterial genomes.
The developed framework enhances the accuracy and reliability of VF prediction.
DeepVF serves as a valuable tool for screening potential virulence factors in bacterial genome-wide studies.