SEMtree: tree-based structure learning methods with structural equation models.

ABSTRACT:

Motivation

With the exponential growth of expression and protein-protein interaction (PPI) data, the identification of functional modules in PPI networks that show striking changes in molecular activity or phenotypic signatures becomes of particular interest to reveal process-specific information that is correlated with cellular or disease states. This requires both the identification of network nodes with reliability scores and the availability of an efficient technique to locate the network regions with the highest scores. In the literature, a number of heuristic methods have been suggested. We propose SEMtree(), a set of tree-based structure discovery algorithms, combining graph and statistically interpretable parameters together with a user-friendly R package based on structura

SUBMITTER: Grassi M 

PROVIDER: S-EPMC10287946 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature