miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

Authors

Mauricio Soto (ORCID: https://orcid.org/0000-0001-5977-9467)
Jessica Gliozzo (ORCID: https://orcid.org/0000-0001-7629-8112)
Giorgio Valentini (ORCID: https://orcid.org/0000-0002-5694-3919)
Alex Patak (ORCID: https://orcid.org/0000-0002-9282-188X)
Elena Casiraghi (ORCID: https://orcid.org/0000-0003-2024-7572)
Arturo Bonometti (ORCID: https://orcid.org/0000-0003-3258-0727)

Institutions

Humanitas University (IT)
Lawrence Berkeley National Laboratory (US)
University of Milan (IT)
European Commission (BE)
Nuovo Ospedale San Giovanni di Dio (IT)
Intelligent Systems Research (United States) (US)
Joint Research Centre (DE)
IRCCS Humanitas Research Hospital (IT)

Publication Details

Journal: Bioinformatics
Published: 2025-03-29
DOI: https://doi.org/10.1093/bioinformatics/btaf150
Citations: 2
Primary Topic: AI in cancer detection
Type: article
Field-Weighted Citation Impact: 4.96

Funders

Università degli Studi di Milano
European Commission
Joint Research Centre

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article

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

Mauricio Soto, Jessica Gliozzo, Giorgio Valentini, Alex Patak et al.

Mar 29, 2025

2 citations

Bioinformatics

AI in cancer detection

4.96

article

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

Mauricio Soto, Jessica Gliozzo, Giorgio Valentini, Alex Patak, Elena Casiraghi, Arturo Bonometti

March 29, 2025article en

2 citations

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(4)

Humanitas University (IT), Lawrence Berkeley National Laboratory (US), University of Milan (IT), European Commission (BE), Nuovo Ospedale San Giovanni di Dio (IT), Intelligent Systems Research (United States) (US), Joint Research Centre (DE), IRCCS Humanitas Research Hospital (IT)

Università degli Studi di Milano, European Commission, Joint Research Centre

Openalex Percentile: Top 6%

AI in cancer detection

4.96

DOI Open Access PDF OpenAlex

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