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Related Concept Videos

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Related Experiment Video

Updated: Jul 10, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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VF-Pred: Predicting virulence factor using sequence alignment percentage and ensemble learning models.

Shreya Singh1, Nguyen Quoc Khanh Le2, Cheng Wang1

  • 1NUS-ISS, National University of Singapore, 119615, Singapore.

Computers in Biology and Medicine
|November 18, 2023
PubMed
Summary
This summary is machine-generated.

This study presents VF-Pred, a new framework for detecting virulence factors (VFs) in pathogens using genomic data. VF-Pred achieves high accuracy in identifying VFs, aiding in the development of new drugs and vaccines against infectious diseases.

Keywords:
Ensemble learningFeature engineeringMachine learningProtein sequence analysisSequence alignmentVirulence factors

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Virulence factors (VFs) are essential for pathogen infection and immune evasion, driving infectious diseases.
  • Accurate identification of VFs is critical for developing effective therapeutics and vaccines.
  • Existing methods for VF detection require improvement in accuracy and efficiency.

Purpose of the Study:

  • To introduce VF-Pred, a novel computational framework for detecting virulence factors (VFs) using genomic data.
  • To enhance the accuracy and reliability of virulence factor identification through advanced machine learning techniques.
  • To provide a user-friendly tool for predicting the likelihood of VFs in pathogens.

Main Methods:

  • VF-Pred integrates diverse feature engineering techniques, including a novel Seq-Alignment feature.
  • A comprehensive ensemble of 25 machine learning models was trained on 982 engineered features.
  • A unique downstream ensembling approach consolidates model predictions for superior performance.

Main Results:

  • VF-Pred demonstrated superior performance compared to existing ensembling strategies.
  • The framework achieved a high accuracy of 83.5% and sensitivity of 87% in virulence factor identification.
  • The novel Seq-Alignment feature significantly boosted the accuracy of the machine learning algorithms.

Conclusions:

  • VF-Pred offers a highly promising and accurate methodology for identifying virulence factors from genomic data.
  • The framework's advanced ensembling technique surpasses current methods in VF detection.
  • VF-Pred has the potential to accelerate the development of novel strategies against infectious diseases.