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Updated: Jun 10, 2026

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
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Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

Raul Cruz-Cano1, David S H Chew, Choi Kwok-Pui

  • 1Department of Computer and Information Sciences, Texas A&M University-Texarkana, Texarkana, TX, 75501, USA, Raul.Cruz-Cano@tamut.edu.

INFORMS Journal on Computing
|August 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel least-squares support vector machine (LS-SVM) method for predicting viral DNA replication origins. The LS-SVM approach demonstrates superior or comparable accuracy to existing methods across herpesviruses and caudoviruses.

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Combining Analysis of DNA in a Crude Virion Extraction with the Analysis of RNA from Infected Leaves to Discover New Virus Genomes

Published on: July 27, 2018

Area of Science:

  • Virology
  • Computational Biology
  • Bioinformatics

Background:

  • Viral DNA replication is crucial for viral reproduction.
  • Identifying viral replication origins is key to controlling viral spread.
  • Current computational methods for viral replication origin prediction are primarily tested on herpesviruses.

Purpose of the Study:

  • To propose and evaluate a new computational approach for predicting viral DNA replication origins using least-squares support vector machines (LS-SVMs).
  • To assess the performance of LS-SVMs on diverse viral families, including herpesviruses and caudoviruses.
  • To identify significant features influencing viral replication origin prediction.

Main Methods:

  • Least-squares support vector machines (LS-SVMs) were employed for prediction.
  • The LS-SVM approach was tested on replication origin data from herpesviruses and caudoviruses (three families).
  • Recursive feature elimination was utilized to identify significant predictive features.

Main Results:

  • The LS-SVM approach achieved sensitivities and positive predictive values superior or comparable to existing methods.
  • Combining LS-SVMs with previous methods further enhanced prediction accuracy for herpesvirus origins.
  • Recursive feature elimination successfully identified key features within the datasets.

Conclusions:

  • LS-SVMs represent a valuable computational tool for viral replication origin prediction.
  • The study highlights the utility of optimization-based computing techniques in biomedical applications.
  • This method offers improved accuracy and feature identification for viral studies.