Protocol for using treeLFA to infer multimorbidity patterns in the form of disease topics from diagnosis data in biobanks
Year: 2025
Type: article
Abstract: 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
Source: STAR Protocols
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, University of Oxford, Harvard University, University of Cambridge, British Heart Foundation, Ellison Institute of Technology, University Medical Center Groningen, University of Groningen
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,550
OpenAlex ID: W4414042238