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Table 4 Signatures detected in top 20 ranked features (Mouse)

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

Signatue # Algorithm groups BASIC CONS NUCLEO ORF REPS AUPR (AUC)
1 IG TxNex phm, phmn, ACA, ACG, KOZAK   0.47 (0.92)
    phmx, CCG, CG,    
    py60m, CGA, CGC,    
    py60mx CGG, CGT,    
     FickScore, GC,    
     GCG, TAA,    
2 GR   phm, phmn, ACA, AGA,   DNA.hAT.Charlie, 0.40 (0.91)
    phmx, AT, CA, CAA,   LINE.RTE.BovB,  
    py60m, CAT, CG, TGA   LINE.RTE.X,  
    py60mx    LTR.ERVL.MaLR,  
3 RFS TxExLenAvg, phm, phmn, AA, FickScore KOZAK, LINE.L1, 0.44 (0.92)
   TxLen, phmx,   OrfProp LTR.ERV1,  
   TxNex py60m,    LTR.ERVK,  
    py60mx    LTR.ERVL,  
       SINE.B2, SINE.B4  
4 GFS, LR, TxExLenAvg, phm, phmn, AAC, AAG, KOZAK   0.51 (0.93)
  EN TxLen, phmx, AC, ACA, ACT,    
   TxNex py60m, AGT, CAC,    
    py60mx CAG, CAT,    
     CGT, CTT,    
     GAT, GT,    
     GTA, GTC,    
     GTG, TAA,    
     TAC, TAT    
5 RF, TxExLenAvg, phm, phmn, AA, AC, KOZAK   0.51 (0.93)
  EFmn TxLen, phmx, ACA, AGA,    
   TxNex py60m, CAC, CAT,    
    py60mx CCG, CG,    
     CGC, CGG,    
     FickScore, GC,    
     GGC, GT,    
     TAA, TAT, TT    
6 WT TxExLenAvg, AAC, AAG,     0.46 (0.92)
   TxNex   AAT, AC,    
     ACA, ACC,    
     ACG, ACT,    
     AGA, AGC,    
     AT, CA, CG,    
     CT, GT, TA,    
     TC, TG