Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. 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.

Heterogeneous data integration methods for patient similarity networks / J. Gliozzo, M. Mesiti, M. Notaro, A. Petrini, A. Patak, A. Puertas-Gallardo, A. Paccanaro, G. Valentini, E. Casiraghi. - In: BRIEFINGS IN BIOINFORMATICS. - ISSN 1467-5463. - (2022 Jun 13). [Epub ahead of print] [10.1093/bib/bbac207]

Heterogeneous data integration methods for patient similarity networks

J. Gliozzo
Primo
;
M. Mesiti;M. Notaro;A. Petrini;G. Valentini
Penultimo
;
E. Casiraghi
Ultimo
2022-06-13

Abstract

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. 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.
biomedical applications; data fusion; multimodal data; patient similarity networks
Settore INF/01 - Informatica
Settore MED/01 - Statistica Medica
PSR2015-1720GVALE_01 - PIANO DI SOSTEGNO ALLA RICERCA 2015-2017 - TRANSITION GRANT LINEA 1A PROGETTO "UNIMI PARTENARIATI H2020" (anno 2020) - VALENTINI, GIORGIO - PSR2015-17 - Piano di sviluppo di ricerca 2015-17 - 2020
13-giu-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/930904
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