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

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Published on: September 25, 2021

Harnessing Machine Learning for Agnostic Biodetection.

Sarah H Sandholtz¹, Camilo Valdes¹, Nisha Mulakken¹

¹Sarah H. Sandholtz, PhD, is a Staff Scientist; Camilo Valdes, PhD, is a Postdoctoral Researcher; Jeffrey A. Drocco, PhD, is Group Leader, Advanced Biotechnologies Integration Group; Crystal Jaing, PhD, is Group Leader, Genomics Group; and Nicholas A. Be, PhD, is Group Leader, Microbiology/Immunology Group; all in the Biosciences and Biotechnology Division, Physical and Life Sciences Directorate. Nisha Mulakken, MA, is Deputy Division Leader; Marisa W. Torres, MS, is Bioinformatics Lead; Aram Avila-Herrera, PhD, is Group Leader, Biomolecular Design and Development Group; Jose Manuel Martí, PhD, is a Staff Scientist; and Jonathan E. Allen, PhD, is a Senior Technical Staff Member; all in the Global Security Computing Applications Division, Computing Directorate. Uttara Tipnis, PhD, is a Staff Scientist, Computational Engineering Division, Engineering Directorate. All of the authors are at Lawrence Livermore National Laboratory, Livermore, CA.

Health Security

|May 30, 2025

Summary

This summary is machine-generated.

The US biodefense strategy needs an agent-agnostic approach for detecting novel threats. Machine learning (ML) offers a promising solution for adaptable environmental biodetection systems.

Keywords:

Agent agnostic Biodetection Biosurveillance Machine learning

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Area of Science:

Biodefense and Environmental Monitoring
Computational Biology and Machine Learning

Background:

Current US biodefense relies on identifying known biological agents, limiting its effectiveness against novel threats.
An agent-agnostic approach using signatures offers greater adaptability to evolving biological threats.
Machine learning (ML) excels at pattern recognition across diverse data, showing potential for biodetection.

Purpose of the Study:

To review current machine learning (ML) platforms for environmental biodetection.
To identify development needs for ML-enabled, agent-agnostic biodetection.
To support a transition from list-based to signature-based biodefense strategies.

Main Methods:

Systematic literature review of existing ML platforms applicable to biodetection.
Analysis of ML capabilities for recognizing complex patterns from multimodal data.
Discussion of technical requirements for ML in environmental biodetection.

Main Results:

Identified current ML platforms and their potential for biodetection applications.
Highlighted key technical capabilities and limitations of existing ML systems.
Outlined necessary advancements for effective ML-driven agnostic biodetection.

Conclusions:

Transitioning to ML-enabled, agent-agnostic biodetection is crucial for enhanced national security.
Further development is required to fully leverage ML for adaptable environmental threat detection.
A systematic understanding of ML capabilities is essential for future biodefense innovation.