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These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.<\/jats:p>","DOI":"10.1093\/bib\/bbac207","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T19:16:35Z","timestamp":1652210195000},"source":"Crossref","is-referenced-by-count":34,"title":["Heterogeneous data integration methods for patient similarity networks"],"prefix":"10.1093","volume":"23","author":[{"given":"Jessica","family":"Gliozzo","sequence":"first","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"European Commission, Joint Research Centre (JRC) , Ispra (VA) , Italy"},{"name":"CINI, Infolife National Laboratory , Roma , Italy"}]},{"given":"Marco","family":"Mesiti","sequence":"additional","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"CINI, Infolife National Laboratory , Roma , Italy"}]},{"given":"Marco","family":"Notaro","sequence":"additional","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"CINI, Infolife National Laboratory , Roma , Italy"}]},{"given":"Alessandro","family":"Petrini","sequence":"additional","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"CINI, Infolife National Laboratory , Roma , Italy"}]},{"given":"Alex","family":"Patak","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC) , Ispra (VA) , Italy"}]},{"given":"Antonio","family":"Puertas-Gallardo","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC) , Ispra (VA) , Italy"}]},{"given":"Alberto","family":"Paccanaro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Royal Holloway, University of London , Egham, TW20 0EX UK"},{"name":"School of Applied Mathematics (EMAp), Funda\u00e7\u00e3o Get\u00falio Vargas , Rio de Janeiro Brazil"}]},{"given":"Giorgio","family":"Valentini","sequence":"additional","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"CINI, Infolife National Laboratory , Roma , Italy"},{"name":"DSRC UNIMI, Data Science Research Center , Milano, 20135 , Italy"},{"name":"ELLIS, European Laboratory for Learning and Intelligent Systems , Berlin , Germany"}]},{"given":"Elena","family":"Casiraghi","sequence":"additional","affiliation":[{"name":"AnacletoLab - Computer Science Department, Universit\u00e1 degli Studi di Milano , Via Celoria 18, 20135, Milan , Italy"},{"name":"CINI, Infolife National 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