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Multi-omics molecules regulate complex biological processes (CBPs), which reflect the activities of various molecules in living organisms. Meanwhile, the applications to represent disease subtypes and cell types have created an urgent need for sample grouping and associated CBP-inferring tools. In this paper, we present CBP-JMF, a practical tool primarily for discovering CBPs, which underlie sample groups as disease subtypes in applications. Differently from existing methods, CBP-JMF is based on a joint non-negative matrix tri-factorization framework and is implemented in Python. As a pragmatic application, we apply CBP-JMF to identify CBPs for four subtypes of breast cancer. The result shows significant overlapping between genes extracted from CBPs and known subtype pathways. We verify the effectiveness of our tool in detecting CBPs that interpret subtypes of disease.

CBP-JMF: An Improved Joint Matrix Tri-Factorization Method for Characterizing Complex Biological Processes of Diseases