To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov).  DeepAutoCov identifies Future Dominant Lineages (FDLs), defined as lineages comprising at least 25% of SARS-CoV-2 genomes added on a given week, on a weekly basis, using the Spike (S) protein.  We developed two concurrent approaches (a linear unsupervised and a posteriori supervised) to evaluate DeepAutoCoV performance.  DeepAutoCoV identifies FDL, using the spike (S) protein, with a median lead time of 31 weeks on global data and achieves a positive predictive value ∼7x better and 23% higher than the other approaches.  Finally, DeepAutoCoV is not only predictive but also interpretable, since it can pinpoint specific mutations within FDLs, generating hypotheses on the potential increases in virulence or transmissibility of a lineage. 
DeepAutoCov
To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov). 
Deep learning
To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov). 
autoencoder-based method
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.The COVID-19 pandemic exemplified the need for a rapid, effective genomic-based surveillance system to predict emerging SARS-CoV-2 variants and lineages.  To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov).  Our algorithm is grounded on anomaly detection via an unsupervised approach, which is necessary given that FDLs can be known only a posteriori (i.e., after they have become dominant). 
anomaly detection
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.The COVID-19 pandemic exemplified the need for a rapid, effective genomic-based surveillance system to predict emerging SARS-CoV-2 variants and lineages.  To bridge this gap, we introduce a novel Deep learning, autoencoder-based method for anomaly detection in SARS-CoV-2 (DeepAutoCov).  Trained and updated on the public global SARS-CoV-2 GISAID database.  DeepAutoCov identifies Future Dominant Lineages (FDLs), defined as lineages comprising at least 25% of SARS-CoV-2 genomes added on a given week, on a weekly basis, using the Spike (S) protein. 
SARS-CoV-2
Trained and updated on the public global SARS-CoV-2 GISAID database. 
GISAID database