Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data.

ABSTRACT: Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about their biology. Yet, as more experiments are considered, a complex's signature may become conditional and less identifiable. Previously we successfully distinguished the general proteomic signature of genuine chromosomal proteins from hitchhikers using the Random Forests (RF) machine learning algorithm. Here we test whether small protein complexes can define distinguishable signatures of their own, despite the assumption that machine learning needs large training sets. We show, with simulated and real proteomics data, that RF can detect small protei

SUBMITTER: Montano-Gutierrez LF 

PROVIDER: S-EPMC5328625 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature