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Table 2 Performances of different feature combination models

From: Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods

Feature

Recall

Precision

ACC

F

AUC

AUPR

SP

0.545

0.455

0.958

0.477

0.874

0.483

SP + Markov

0.528

0.479

0.961

0.482

0.887

0.492

SP + Markov + DN

0.530

0.484

0.961

0.486

0.889

0.498

SP + Markov + DN + PWM

0.505

0.507

0.963

0.487

0.889

0.500

All

0.532

0.478

0.961

0.484

0.884

0.494

  1. Markov Markov motif profile, PWM position weight matrix profile, DN dinucleotide profile, SP sparse profile, PPT polypyrimidine tract, combination combining all features