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Related Experiment Video

Updated: May 13, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples.

Shorya Consul¹, John Robertson¹, Haris Vikalo¹

¹Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|May 20, 2025

Summary

This summary is machine-generated.

A new deep learning tool, XVir, accurately detects viral DNA in human tumors. This advancement aids in understanding the 15% of cancers linked to viral infections, improving cancer research and diagnostics.

Keywords:

cancer classification transformers viruses

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Oncology
Virology
Bioinformatics
Computational Biology

Background:

Approximately 15% of global cancers are linked to viral infections, involving oncoviruses like HPV and HBV.
Advancements in sequencing technologies enable analysis of tumor DNA for cancer-virus associations.
Detecting diverse viral DNA in tumors is challenging for computational methods and machine learning models.

Purpose of the Study:

To introduce XVir, a novel data pipeline for reliable identification of viral DNA in human tumors.
To develop a robust deep learning model for detecting oncoviruses in complex genomic data.
To improve the computational analysis of viral contributions to cancer.

Main Methods:

Developed XVir, a data pipeline utilizing a transformer-based deep learning architecture.
Trained the model on mixed sequencing reads from viral and human genomes.
Evaluated performance on semi-experimental data, comparing against state-of-the-art methods.

Main Results:

XVir achieves high classification accuracy in detecting viral DNA in human tumors.
The model demonstrates robust performance across diverse viral populations and experimental settings.
XVir outperforms competing methods and is significantly faster to train.

Conclusions:

XVir offers a reliable and efficient solution for identifying oncoviral DNA in tumor samples.
The deep learning approach enhances the study of virus-driven cancers.
This tool has the potential to advance cancer diagnostics and research into viral oncogenesis.