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Neural network based integration of assays to assess pathogenic potential
Mohammed Eslami1, Yi-Pei Chen2, Ainsley C Nicholson3
1Netrias, LLC, 1162 Gateway Drive, Annapolis, MD, 21409, USA. meslami@netrias.com.
Scientific Reports
|April 13, 2023
Summary
A new deep learning model, the neural network embedding model (NNEM), improves bacterial biothreat assessment by integrating diverse data. This approach enhances accuracy in identifying dangerous bacterial strains.
Area of Science:
- Microbiology
- Bioinformatics
- Machine Learning
Background:
- Accurate biothreat assessment of novel bacterial strains is hampered by limited, disparate data.
- Integrating data from conventional species identification assays with novel pathogenicity assays presents integration challenges.
Purpose of the Study:
- To develop a deep learning approach, the neural network embedding model (NNEM), for integrating diverse datasets to improve bacterial biothreat assessment.
- To enhance the capability of identifying dangerous bacterial strains by leveraging existing and new assay data.
Main Methods:
- Developed a neural network embedding model (NNEM) utilizing deep learning.
- Integrated metabolic data from Special Bacteriology Reference Laboratory (SBRL) assays with pathogenicity assays.
- Transformed assay results into vectors for data enrichment.
Main Results:
- Achieved a 9% improvement in accuracy for biothreat assessment.
- Demonstrated successful integration of data from conventional and pathogenicity assays.
- Validated the NNEM's effectiveness on a large, albeit noisy, dataset.
Conclusions:
- The NNEM provides a generalizable framework for enriching datasets with historical assay data for improved biothreat assessment.
- The model shows promise for enhancing the identification of dangerous bacterial strains.
- Future improvements are expected with the development of new pathogenicity assays.
