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Table 2 Univariate ranked features according to their AUPR (AUC)

From: Identification of long non-coding transcripts with feature selection: a comparative study

  Human Mouse Zebrafish
  Feature AUPR (AUC) Feature AUPR (AUC) Feature AUPR (AUC)
1 ph100m 0.62 (0.92) phm 0.43 (0.90) py8m 0.27 (0.83)
2 ph20m 0.54 (0.91) py60m 0.36 (0.90) ph8m 0.25 (0.79)
3 ph20mx 0.52 (0.89) phmx 0.34 (0.87) TxLen 0.18 (0.72)
4 py100mx 0.52 (0.91) py60mx 0.32 (0.88) FickScore 0.17 (0.73)
5 py100m 0.48 (0.91) phmn 0.25 (0.81) TxNex 0.16 (0.77)
6 py20m 0.43 (0.89) CG 0.16 (0.70) GG 0.15 (0.66)
7 TxNex 0.26 (0.76) GCG 0.15 (0.68) TAA 0.15 (0.67)
8 ph20mn 0.25 (0.77) CGC 0.14 (0.67) AAT 0.15 (0.65)
9 CG 0.24 (0.69) CGA 0.14 (0.67) GAG 0.15 (0.65)
10 FickScore 0.23 (0.76) CCG 0.13 (0.67) GGA 0.14 (0.65)
11 CGA 0.22 (0.68) CGG 0.13 (0.68) KOZAK 0.14 (0.67)
12 TCG 0.21 (0.66) ACA 0.13 (0.63) GGC 0.13 (0.65)
13 CCG 0.21 (0.67) FickScore 0.13 (0.73) TCG 0.13 (0.63)
14 TxLen 0.19 (0.66) TCG 0.13 (0.65) ATT 0.13 (0.63)
15 KOZAK 0.17 (0.65) CGT 0.12 (0.63) CG 0.13 (0.62)
16 CGT 0.17 (0.62) GC 0.12 (0.65) TTG 0.13 (0.59)
17 ACA 0.17 (0.60) CAT 0.12 (0.59) TGG 0.13 (0.64)
18 ACG 0.17 (0.63) ACG 0.12 (0.64) CGG 0.13 (0.63)
19 ACT 0.16 (0.60) ACT 0.12 (0.61) CGA 0.13 (0.62)
20 TCT 0.16 (0.61) GGC 0.11 (0.64) CCG 0.12 (0.62)
21 TGG 0.15 (0.61) TxNex 0.11 (0.73) TT 0.12 (0.61)
22 AAT 0.15 (0.63) KOZAK 0.10 (0.65) TA 0.12 (0.62)
23 GTG 0.15 (0.60) CTA 0.10 (0.59) AG 0.12 (0.60)
24 GG 0.15 (0.62) TxLen 0.10 (0.64) AT 0.12 (0.62)
25 ATA 0.15 (0.61) AC 0.09 (0.59) CAG 0.12 (0.58)