sub:assertion {
  <https://osf.io/bxae4> dct:issued "2024" ;
    dct:license <http://purl.org/np/RAQ__sGdY_Qc7l1O_zmn4nr-pMBOxKU04Ur9s998rS6Fc#CC-BY-4.0> ;
    <https://w3id.org/fdof/ontology#hasEncodingFormat> <https://www.iana.org/assignments/media-types/application/pdf> ;
    <https://w3id.org/fdof/ontology#materializes> sub:STAYAHEAD_M ;
    <https://www.w3.org/ns/dcat#accessURL> <https://osf.io/bxae4> .
  sub:STAYAHEAD_M dct:contributor orcid:0000-0001-8888-635X ;
    dct:creator orcid:0009-0004-2188-0817 ;
    dct:hasVersion "v1.0" ;
    dct:language <https://www.omg.org/spec/LCC/Languages/LaISO639-1-LanguageCodes/en> ;
    dct:publisher <https://ror.org/008xxew50> ;
    dct:subject <http://aims.fao.org/aos/agrovoc/c_4318> ;
    a <https://w3id.org/fair/ff/terms/Method> , <https://w3id.org/fdof/ontology#FAIRDigitalObject> ;
    rdfs:comment "The SARS-CoV-2 outbreak showed the world that we were unable to predict the course of the pandemic. This study is a part of a project that aims at developing methodologies to predict viral variants of high risk in an effort to stay ahead of the next pandemic. We investigate the feasibility of computationally predicting the structures of single mutation variants with AlphaFold-2 and ESMFold and using this information as input to a XGBoost regression model that predicts empirical biophysical properties associated with high-risk Omicron variants. This study illustrates that data intensive regression models when coupled with structure prediction tools, can to contribute to pandemic preparedness." ;
    rdfs:label "STAYAHEAD Dataset Methodology" ;
    <https://schema.org/funder> <https://ror.org/056cwr036> ;
    <https://w3id.org/fdof/ontology#hasMetadata> this: ;
    <https://www.w3.org/ns/dcat#contactPoint> "info@health-holland.com" .
}