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Table 1 Computational methods to predict features

From: Machine learning prediction of oncology drug targets based on protein and network properties

Name of the method

Predicted Feature

Reference website

NetPhos [45]

phosphorylation sites

http://www.cbs.dtu.dk/services/NetPhos/

GlycoMine [46]

glycosylation sites

http://glycomine.erc.monash.edu/Lab/GlycoMine/

WESA [47]

solvent accessibility

http://pipe.scs.fsu.edu/wesa/

Garnier [48]

secondary protein structure

http://www.bioinformatics.nl/cgi-bin/emboss/garnier

Epestfind [41]

PEST motif

http://emboss.bioinformatics.nl/cgi-bin/emboss/epestfind

SignalP [49]

signal peptide cleavage site

http://www.cbs.dtu.dk/services/SignalP/

CELLO [50]

cellular sub-localization

http://cello.life.nctu.edu.tw/

TMHMM [51]

presence of transmembrane helices

http://www.cbs.dtu.dk/services/TMHMM/