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

ABSTRACT: This project has integrated SILAC proteomics data of wild type and knockout mitotic chromosomes, and used machine learning technique random forests to identify chromosomal complexes with high precision, and small training sets.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Gallus Gallus (chicken)

TISSUE(S): Cell Culture

SUBMITTER: Luis Montano

LAB HEAD: Juri Rappsilber

PROVIDER: PXD003588 | Pride | 2018-10-26

REPOSITORIES: Pride

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			Action	DRS
	110312_07_Orbi2_FLA_CO_275_Shynia_Scc1_pH3.raw	Raw
	110312_08_Orbi2_FLA_CO_275_Shynia_Scc1_pH4.raw	Raw
	110312_09_Orbi2_FLA_CO_275_Shynia_Scc1_pH5.raw	Raw
	110312_10_Orbi2_FLA_CO_275_Shynia_Scc1_pH6.raw	Raw
	110312_11_Orbi2_FLA_CO_275_Shynia_Scc1_pH8.raw	Raw

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

Montaño-Gutierrez Luis F LF Ohta Shinya S Kustatscher Georg G Earnshaw William C WC Rappsilber Juri J

Molecular biology of the cell 20170105 5

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 th ...[more]

PMID: 28057767

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