A statistical framework for recovering pseudo-dynamic networks from static data.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac038
Abstract: MOTIVATION The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting… read more here.
Keywords: dynamic networks; framework recovering; recovering pseudo; pseudo dynamic ... See more keywords