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. Author manuscript; available in PMC: 2021 Dec 17.
Published in final edited form as: SoftwareX. 2021 Sep 25;16:100811. doi: 10.1016/j.softx.2021.100811

Figure 3.

Figure 3

Depiction of the major functionalities of pySuStaIn, as a sequence of operations that begins with an input biomarker data matrix, followed by a data preparation step that depends on the chosen data likelihood, then the SuStaIn algorithm run on both full and cross-validated data and finally a set of outputs consisting of: (i) visualizations of the inferred models; (ii) estimates of the most likely subtype and stage for training and test subjects; and (iii) a set of model selection tools.