Prediction of donor splice sites using random forest with a new sequence encoding approach

Meher, Prabina Kumar; Sahu, Tanmaya Kumar; Rao, Atmakuri Ramakrishna

doi:10.1186/s13040-016-0086-4

BioData Mining

Table 6 P-values of the Mann Whitney statistic to test the significant difference between the performance of RF with that of Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers in all the three encoding procedures under both balanced and imbalanced situations

From: Prediction of donor splice sites using random forest with a new sequence encoding approach

$D	EP	CLs	TPR	TNR	F (α = 1)	F (β = 2)	G-mean	WA	MCC
Balanced	P-1	RF-BG	0.343066	0.676435	0.272856	0.212122	0.185711	0.240436	0.272856
		RF-BS	0.820063	0.939006	0.314999	0.795936	0.314999	0.383598	0.314999
		RF-LG	0.001672	0.053092	0.002879	0.000725	0.005196	0.009082	0.005196
		RF-NB	0.000242	0.002796	1.08E-05	1.08E-05	1.08E-05	0.000181	1.08E-05
		RF-KN	0.053182	0.087051	0.028806	0.063013	0.035463	0.025581	0.028806
	P-2	RF-BG	0.41319	0.594314	0.356232	0.356232	0.277512	0.315378	0.356232
		RF-BS	0.837765	0.367844	0.968239	0.968239	0.842105	0.743537	0.842105
		RF-LG	0.000275	0.000439	2.17E-05	2.17E-05	2.17E-05	2.17E-05	2.17E-05
		RF-NB	0.000275	0.004216	2.17E-05	2.17E-05	2.17E-05	2.17E-05	2.17E-05
		RF-KN	0.000376	0.000273	2.17E-05	2.17E-05	2.17E-05	0.000278	2.17E-05
	P-3	RF-BG	0.171672	0.879378	0.14314	0.14314	0.165494	0.15062	0.14314
		RF-BS	0.494174	0.381613	0.970512	0.528849	0.853428	0.820197	0.911797
		RF-LG	0.000181	0.000181	1.08E-05	1.08E-05	1.08E-05	1.08E-05	1.08E-05
		RF-NB	0.000182	0.000279	1.08E-05	1.08E-05	1.08E-05	0.000182	1.08E-05
		RF-KN	0.000182	0.000181	1.08E-05	1.08E-05	1.08E-05	0.000182	1.08E-05
Imbalanced	P-1	RF-BG	0.000269	0.000251	2.17E-05	2.17E-05	2.17E-05	0.000278	2.17E-05
		RF-BS	0.000176	0.002555	0.000181	0.000181	0.000181	0.000178	0.000181
		RF-LG	0.000263	0.000268	2.17E-05	2.17E-05	2.17E-05	0.000263	2.17E-05
		RF-NB	0.000271	0.177338	2.17E-05	2.17E-05	2.17E-05	2.17E-05	2.17E-05
		RF-KN	0.000175	0.025526	1.08E-05	1.08E-05	1.08E-05	0.000182	0.000179
	P-2	RF-BG	0.000179	0.000173	0.000182	0.000182	0.000182	0.000181	0.000182
		RF-BS	0.000181	0.000158	1.08E-05	1.08E-05	1.08E-05	0.000181	1.08E-05
		RF-LG	0.00018	0.000178	1.08E-05	1.08E-05	1.08E-05	0.00018	1.08E-05
		RF-NB	0.000182	0.733634	1.08E-05	1.08E-05	1.08E-05	0.000182	1.08E-05
		RF-KN	0.000181	0.000174	1.08E-05	1.08E-05	1.08E-05	0.000182	1.08E-05
	P-3	RF-BG	0.000176	0.000168	0.000182	0.000182	0.000182	0.000181	0.000182
		RF-BS	0.000179	0.000149	1.08E-05	1.08E-05	1.08E-05	1.08E-05	1.08E-05
		RF-LG	0.000179	0.000177	1.08E-05	1.08E-05	1.08E-05	0.000182	1.08E-05
		RF-NB	0.000177	0.009082	1.08E-05	1.08E-05	1.08E-05	1.08E-05	1.08E-05
		RF-KN	0.00018	0.00018	1.08E-05	1.08E-05	1.08E-05	0.000178	1.08E-05

$D data type, RF random forest, CLs classifiers, BG bagging, BS boosting, LG logistic regression, NB naïve bayes, KN K nearest neighbor

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ISSN: 1756-0381

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