An iteration method for identifying yeast essential proteins from heterogeneous network

BMC Bioinformatics

Table 1 Common and different genes predicted by RWHN and other competing methods ranked in top 200 proteins

Centrality measures (Mi)	\|RWHN∩Mi\|	\|Mi − RWHN \|	Non-essential proteins in {Mi − RWHN}	Percentage of non-essential proteins in {Mi − RWHN} with low RWHN value
DC	27	173	118	83.90%
IC	26	174	118	84.75%
SC	24	176	120	87.50%
BC	23	177	118	89.83%
CC	23	177	110	89.09%
NC	44	156	73	86.30%
PeC	68	132	53	79.25%
CoEWC	69	131	51	76.47%
POEM	69	131	46	71.74%
ION	110	90	40	82.50%

This table shows the common and the difference between RWHN and the ten other competing methods (DC, IC, SC, BC, CC, NC, PeC, CoEWC, POEM and ION) when predicting top 200 proteins. |RWHN∩Mi | denotes the number of proteins identified by both RWHN and one of the ten other methods Mi. {Mi − RWHN} represents the set of proteins detected by Mi while ignored by RWHN. |Mi − RWHN| is the number of proteins in set {Mi − RWHN}. The last column describes the percentages of different nonessential proteins with low RWHN scores (less than 0.2) in top 200 proteins

ISSN: 1471-2105