Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity
Biology (Basel, Switzerland), v 11(12), p1786
08 Dec 2022
PMID: 36552295
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.
Metrics
Details
- Title
- Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity
- Creators
- Bahrad A Sokhansanj - Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Drexel University, Philadelphia, PA 19104, USAZhengqiao Zhao - Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Drexel University, Philadelphia, PA 19104, USAGail L Rosen - Ecological and Evolutionary Signal-Processing and Informatics Laboratory, Department of Electrical & Computer Engineering, College of Engineering, Drexel University, Philadelphia, PA 19104, USA
- Publication Details
- Biology (Basel, Switzerland), v 11(12), p1786
- Publisher
- MDPI
- Grant note
- 2107108 / National Science Foundation 1919691 / National Science Foundation 1936782 / National Science Foundation
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000902305500001
- Scopus ID
- 2-s2.0-85144727503
- Other Identifier
- 991019425982704721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biology