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Defining subtypes of complex diseases such as cancer and stratifying patientgroups with the same disease but different subtypes for targeted treatments isimportant for personalized and precision medicine. Approaches that incorporatemulti-omic data are more advantageous to those using only one data type forpatient clustering and disease subtype discovery. However, it is challenging tointegrate multi-omic data as they are heterogeneous and noisy. In this paper,we present Affinity Network Fusion (ANF) to integrate multi-omic data forpatient clustering. ANF first constructs patient affinity networks for eachomic data type, and then calculates a fused network for spectral clustering. Weapplied ANF to a processed harmonized cancer dataset downloaded from GDC dataportal consisting of 2193 patients, and generated promising results onclustering patients into correct disease types. Moreover, we developed asemi-supervised model combining ANF and neural network for few-shot learning.In several cases, the model can achieve greater than 90% acccuracy on test setwith training less than 1% of the data. This demonstrates the power of ANF inlearning a good representation of patients, and shows the great potential ofsemi-supervised learning in cancer patient clustering.

Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering