Dataset Information

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

ABSTRACT: The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.

SUBMITTER: Nagpal S

PROVIDER: S-EPMC9024110 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

Nagpal Sargun S Pal Ridam R Ashima Tyagi Ananya A Tripathi Sadhana S Nagori Aditya A Ahmad Saad S Mishra Hara Prasad HP Malhotra Rishabh R Kutum Rintu R Sethi Tavpritesh T

Frontiers in genetics 20220408

The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present <i>Strainflow</i>, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. <i>Strainflow</i> was trained ...[more]

PMID: 35464852

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Project description:BackgroundControlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources.ObjectiveThe purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.MethodsRaw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances.ResultsGender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model.ConclusionsThe DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.

Dataset Information

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

Publications

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

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