Research on multimorbidity patterns promotes our understanding of the common pathological mechanisms that underlie co-occurring diseases. Here, we present a protocol to infer multimorbidity clusters in the form of disease topics from large-scale diagnosis data using treeLFA, a topic model based on the Bayesian binary non-negative matrix factorization. We describe steps for installing software, preparing input data, and training the model. We then detail post-processing procedures to obtain summarized results for downstream analyses. For complete details on the use and execution of this protocol, please refer to Zhang et al.1.
Journal article
2025-09-19T00:00:00+00:00
6
Bioinformatics, Genetics, Systems biology, computer sciences, Humans, Multimorbidity, Biological Specimen Banks, Bayes Theorem, Software