Related Experiment Video
Updated: May 10, 2026
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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.
Abstract:
The United States' current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.
