Patient Similarity Networks Integration for Partial Multimodal Datasets

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 multiomics 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.