Heterogeneous data integration methods for patient similarity networks

Jessica Gliozzo; Marco Mesiti; Marco Notaro; Alessandro Petrini; Alex Patak; Antonio Puertas-Gallardo; Alberto Paccanaro; Giorgio Valentini; Elena Casiraghi

doi:10.1093/bib/bbac207

Heterogeneous data integration methods for patient similarity networks

Jessica Gliozzo, Marco Mesiti, Marco Notaro, Alessandro Petrini, Alex Patak, Antonio Puertas-Gallardo, Alberto Paccanaro, Giorgio Valentini, Elena Casiraghi

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	bbac207
Journal	Briefings in Bioinformatics
Volume	23
Issue number	4
DOIs	https://doi.org/10.1093/bib/bbac207
Publication status	Published - 13 Jun 2022

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10.1093/bib/bbac207Licence: CC BY

Bilateral NSF/BIO-BBSRC - Translational landscape to link cell growth with proliferation in the root meristem
Bogre, L. (PI) & Paccanaro, A. (PI)
Biotechnology&BioSci Research BBSRC
1/07/15 → 30/06/18
Project: Research
A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families
Paccanaro, A. (PI)
Biotechnology&BioSci Research BBSRC
1/07/12 → 30/09/13
Project: Research
Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data
Paccanaro, A. (PI) & Bogre, L. (CoI)
Biotechnology&BioSci Research BBSRC
1/06/08 → 31/05/11
Project: Research

Cite this

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title = "Heterogeneous data integration methods for patient similarity networks",

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AU - Gliozzo, Jessica

AU - Mesiti, Marco

AU - Notaro, Marco

AU - Petrini, Alessandro

AU - Patak, Alex

AU - Puertas-Gallardo, Antonio

AU - Paccanaro, Alberto

AU - Valentini, Giorgio

AU - Casiraghi, Elena

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JO - Briefings in Bioinformatics

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Heterogeneous data integration methods for patient similarity networks

Access to Document

Projects

Bilateral NSF/BIO-BBSRC - Translational landscape to link cell growth with proliferation in the root meristem

A GPU-based high performance system for discovering consensus domain architecture and functional annotation of protein families

Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data

Cite this