Skip to main content

Table 3 The performance of NHMC and competitive methods in predicting gene function for different datasets and PPI networks

From: Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction

 

All genes

Network

 

DIP

BioGRID

Method

CLUS-HMC

NHMC

FF

H

NHMC

FF

H

Dataset

 

α = 0

α = 0.5

  

α = 0

α = 0.5

  

seq

0.030

0.025

0.025

0.003

0.002

0.022

0.022

0.004

0.006

pheno

0.021

0.018

0.019

0.002

0.001

0.018

0.018

0.004

0.002

struc

0.018

0.012

0.016

0.002

0.000

0.012

0.012

0.004

0.002

homo

0.040

0.013

0.031

0.001

0.001

0.013

0.013

0.002

0.002

cellcycle

0.017

0.297

0.273

0.004

0.002

0.013

0.013

0.006

0.006

church

0.017

0.013

0.012

0.003

0.002

0.012

0.012

0.006

0.006

derisi

0.018

0.022

0.021

0.004

0.002

0.039

0.315

0.006

0.006

eisen

0.025

0.020

0.020

0.004

0.002

0.021

0.335

0.006

0.008

gasch1

0.020

0.017

0.017

0.003

0.002

0.029

0.339

0.006

0.006

gasch2

0.019

0.020

0.018

0.004

0.002

0.015

0.016

0.006

0.006

spo

0.018

0.019

0.018

0.004

0.002

0.017

0.017

0.006

0.006

exp

0.020

0.017

0.017

0.002

0.002

0.018

0.018

0.006

0.006

Average:

0.022

0.041

0.041

0.003

0.002

0.019

0.094

0.005

0.005

  1. We use the 3-fold cross-validation evaluation schema. The average AUPRC ¯ (estimated by 3-fold CV) of the CLUS-HMC (α = 1), NHMC (α = 0.5 and α = 0), FunctionalFlow (FF), and Hopfield (H) methods, when predicting gene function in yeast using GO annotations. We use 12 yeast (Saccharomyces cerevisiae) datasets. Results for two PPI networks (DIP and BioGRID) are presented.