Skip to main content

Table 5 The performances of individual feature-based models on imbalanced Human dataset

From: A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs

Index

Feature

AUC

ACC

SN

SP

F1

1-Spectrum Profile

0.748

0.739

0.398

0.854

F2

2-Spectrum Profile

0.841

0.808

0.416

0.940

F3

3-Spectrum Profile

0.850

0.814

0.321

0.982

F4

4-Spectrum Profile

0.844

0.811

0.284

0.989

F5

5-Spectrum Profile

0.836

0.813

0.305

0.986

F6

(3,1)-Mismatch Profile

0.867

0.824

0.427

0.959

F7

(4,1)-Mismatch Profile

0.856

0.814

0.328

0.979

F8

(5,1)-Mismatch Profile

0.851

0.810

0.277

0.991

F9

(3,1)-Subsequence Profile

0.850

0.808

0.443

0.932

F10

(4,1)-Subsequence Profile

0.864

0.822

0.473

0.940

F11

(5,1)-Subsequence Profile

0.871

0.829

0.492

0.944

F12

1-RevcKmer

0.745

0.746

0.005

0.997

F13

2-RevcKmer

0.803

0.778

0.411

0.902

F14

3-RevcKmer

0.823

0.800

0.265

0.981

F15

4-RevcKmer

0.823

0.803

0.241

0.993

F16

5-RevcKmer

0.818

0.806

0.255

0.992

F17

PCPseDNC

0.841

0.806

0.374

0.952

F18

PCPseTNC

0.857

0.813

0.337

0.975

F19

SCPseDNC

0.836

0.803

0.346

0.958

F20

SCPseTNC

0.842

0.808

0.312

0.977

F21

Sparse Profile

0.905

0.856

0.634

0.932

F22

PSSM

0.882

0.832

0.584

0.916

F23

LSSTE

0.688

0.766

0.175

0.966