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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.
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.
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.
