{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:42:09Z","timestamp":1653482529639},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:00:00Z","timestamp":1653436800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,25]]},"abstract":"<jats:p>In this work we show that Incremental Machine Learning can be used to predict the classification of emerging SARS-CoV-2 lineages, dynamically distinguishing between neutral variants and non-neutral ones, i.e. variants of interest or variants of concerns. Starting from the Spike protein primary sequences collected in the GISAID db, we have derived a set of k-mers features, i.e., aminoacid subsequences with fixed length k. We have then implemented a Logistic Regression Incremental Learner that was monthly tested on the variants collected since February 2020 until October 2021. The average value of balanced accuracy of the classifier is 0.72 \u00b1 0.2, which increased to 0.78 \u00b1 0.16 in the last 12 months. The alpha, beta, gamma, eta, kappa and delta variants were recognized as non-neutral variants with mean recall \u223c90%. In summary, incremental learning proved to be a useful instrument for pandemic surveillance, given its capability to update the model on new data over time<\/jats:p>","DOI":"10.3233\/shti220550","type":"book-chapter","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:16:40Z","timestamp":1653481000000},"source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning"],"prefix":"10.3233","author":[{"given":"Giovanna","family":"Nicora","sequence":"first","affiliation":[{"name":"Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy"},{"name":"enGenome S.r.l, Via Ferrata 5, Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Marini","sequence":"additional","affiliation":[{"name":"Dept. of Epidemiology, University of Florida, Gainesville (FL)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Salemi","sequence":"additional","affiliation":[{"name":"Dept. of Pathology, University of Florida, Gainesville (FL)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Bellazzi","sequence":"additional","affiliation":[{"name":"Dept. of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Challenges of Trustable AI and Added-Value on Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T12:16:41Z","timestamp":1653481001000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,25]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220550","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,25]]}}}