A Novel Biclustering Algorithm for the Discovery of Meaningful Biological Correlations between microRNAs and their Target Genes

Table 6 mirDIP_filt results

α	β	$N (% / %)$	level 1				max level					best level					time (s)
			#cc	p _MF	p _BP	µ _q	lev	#cc	p _MF	p _BP	µ _q	lev	#cc	p _MF	p _BP	µ _q
HOCCLUS2
0.1	0.3	46/86	528	0.000	0.000	0.49	8	40	0.027	0.127	0.12	1	528	0.000	0.000	0.49	2031
0.2							6	135	0.001	0.000	0.25	1	528	0.000	0.000	0.49	2225
0.3							5	269	0.000	0.000	0.36	1	528	0.000	0.000	0.49	2824
0.1	0.4	40/85	500	0.000	0.000	0.52	7	35	0.056	0.045	0.12	1	500	0.000	0.000	0.52	1627
0.2							6	121	0.000	0.000	0.25	1	500	0.000	0.000	0.52	1870
0.3							5	225	0.000	0.000	0.35	1	500	0.000	0.000	0.52	2213
0.1	0.5	29/79	503	1.000	1.000	0.58	8	30	0.061	0.215	0.12	8	30	0.061	0.215	0.12	1495
0.2							6	92	0.485	1.000	0.24	2	252	0.188	0.110	0.49	1582
0.3							6	178	0.095	0.215	0.36	4	184	0.046	0.165	0.38	1948
METIS
-	-	100/100	528	1.000	1.000	0.34	-	-	-	-	-	-	-	-	-	-	26
-	-		500	1.000	1.000	0.30	-	-	-	-	-	-	-	-	-	-	27
-	-		503	1.000	1.000	0.31	-	-	-	-	-	-	-	-	-	-	24
ROCC
-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-

mirDIP_filt results. $N (% / %)$ represents the percentage of biclustered mRNAs and miRNAs. #cc is the number of biclusters. lev represents the level number. ROCC results are not reported for filtered datasets since it raised an error during the execution and the biclustering process could not terminate.

ISSN: 1471-2105