Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia.

ABSTRACT:

Objective

Computing patients' similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge.

Materials and methods

In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise

SUBMITTER: Vitali F 

PROVIDER: S-EPMC6951984 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature