Authors:
Jessica Gliozzo
1
;
2
;
Alex Patak
2
;
Antonio Puertas-Gallardo
2
;
Elena Casiraghi
1
and
Giorgio Valentini
1
;
3
Affiliations:
1
AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy
;
2
European Commission, Joint Research Centre (JRC), Ispra, Italy
;
3
ELLIS - European Laboratory for Learning and Intelligent Systems, Milan Unit, Via Celoria 18, 20135, Milan, Italy
Keyword(s):
Patient Similarity Networks, Similarity Network Fusion, Data Integration, Multi-Omics Data Integration, Missing Data, Partial Samples.
Abstract:
Integration of partial samples in Patients Similarity Networks, i.e. the combination of multiple data sources when some of them are completely missing in some samples, is a largely overlooked problem in the multi-omics data integration literature for Precision Medicine. Nevertheless in clinical practice it is quite usual that one or more types of data are missing for a subset of patients. We present an algorithm able to combine multiple sources of data in Patients Similarity Networks when data of one or more sources are completely missing for a subset of patients. The proposed approach relies on a message-passing learning strategy to recover and combine completely missing data leveraging the Similarity Network Fusion algorithm. Preliminary results on TCGA breast cancer data show the effectiveness of the proposed approach.