Analysis of energy-based algorithms for RNA secondary structure prediction

BMC Bioinformatics

Table 2 F-measure prediction accuracy of the MEA, MFE, gC-g1, and gC-pMFmeas algorithms on the MT dataset

RNA class	Class size	Mean ± std of length	F-meas (BL*)				F-meas (Turner99)
			ubcMEA	ubcMFE	gC-g1	gC-pMFmeas	rsMEA	rsMFE
16S Ribosomal RNA	88	377.88 ± 167.18	0.649	0.621	0.640	0.659	0.574	0.539
23S Ribosomal RNA	27	460.6 ± 151.3	0.711	0.683	0.693	0.733	0.681	0.646
5S Ribosomal RNA	309	119.5 ± 2.69	0.739	0.743	0.725	0.746	0.625	0.642
Group I intron	16	344.88 ± 66.42	0.705	0.650	0.674	0.708	0.627	0.599
Group II intron	3	668.7 70.92	0.720	0.739	0.683	0.750	0.744	0.703
Ribonuclease P RNA	6	382.5 ± 41.66	0.471	0.460	0.519	0.495	0.517	0.522
Signal Recognition RNA	91	267.95 ± 61.72	0.641	0.621	0.633	0.637	0.518	0.557
Transfer RNA	484	77.48 ± 4.8	0.718	0.775	0.727	0.782	0.726	0.727
Unweighted Average			0.669	0.662	0.662	0.689	0.627	0.617
Weighted Average			0.710	0.732	0.707	0.743	0.660	0.665
S-Weighted Average			0.670	0.652	0.660	0.684	0.612	0.598

ISSN: 1471-2105