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Table 2 Comparison of predictive accuracy resulted from different screening methods

From: Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Minimum required interaction score

Methods

Hub genes

k

Accuracy of kNN algorithm

Mean accuracy of random forest algorithm (rerun 100 times)

0.700

Method 1: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI-2 hub genes by survival analysis and cox analysis

MMP7, ITGA2

2

78.21%

81.31%

5

84.62%

10

87.18%

23

92.31%

27

93.59%

Method 2: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI

ALB, EGF, FN1, ITGA2, COL1A2, SPARC, COL3A1, TIMP1, COL5A1, COL11A1, MMP7

2

79.49%

83.54%

4

70.51%

6

76.92%

9

78.20%

13

80.77%

15

88.46%

18

83.33%

Method 3: 724 DGEs-genes bearing top 10 degrees in PPI-2 hub genes by survival analysis and cox analysis

TOP2A, MAD2L1

2

65.38%

69.82%

5

69.23%

8

65.38%

12

66.67%

23

67.95%

Method 4: 724 DGEs-genes bearing top 10 degrees in PPI

CCNB1, CCNA2, MAD2L1, TOP2A, UBE2C, CDC20, TTK, MELK, BUB1B, NDC80

2

70.51%

74.81%

5

71.80%

8

76.92%

13

75.64%

23

74.36%

0.400

Method 5: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI-1 hub genes by survival analysis and cox analysis

ITGA2

2

74.36%

69.23%

5

80.77%

10

80.77%

14

80.77%

18

82.05%

22

85.90%

Method 6: 724 DGEs-181 candidate genes-genes bearing top 10 degrees in PPI

ALB, EGF, ITGA2, FN1, COL1A2, TIMP1, MMP1, COL3A1, PTGS2, CEL

2

82.05%

83.72%

4

71.80%

6

79.49%

10

75.64%

13

74.36%

18

73.08%

Method7:724 DGEs-genes bearing top10 degrees in PPI-2 hub genes by survival analysis and cox analysis

TOP2A, MAD2L1

2

65.38%

69.82%

5

69.23%

8

65.38%

12

66.67%

23

67.95%

Method8:724 DGEs-genes bearing top 10 degrees in PPI

ALB, GAPDH, EGF, TOP2A, CCNB1, NDC80, CCNA2, CDC20, UBE2C, BUB1B, MAD2L1, TTK, OIP5, KIF11

2

71.79%

73.05%

6

73.08%

11

69.23%

15

70.51%

22

73.08%

  1. Method 1: Identification of DEGs → screening candidate genes with top 25% variance → construction of PPI by candidate genes with minimum required interaction score at 0.700, and further screen candidate genes with top 10 degrees in PPI → Selection of hub genes by survival and cox analyses in TCGA database
  2. Method 2: Identification of DEGs → screening candidate genes with top 25% variance → construction of PPI by candidate genes with minimum required interaction score at 0.700, and further identification of hub genes bearing top 10 degrees in PPI
  3. Method 3: Identification of DEGs → construction of PPI by candidate genes with minimum required interaction score at 0.700, and further screen candidate genes with top 10 degrees in PPI → selection of hub genes by survival and cox analyses in TCGA database
  4. Method 4: Identification of DEGs → construction of PPI by candidate genes with minimum required interaction score at 0.700, and further identification of hub genes bearing top 10 degrees in PPI
  5. Method 5: Identification of DEGs → screening candidate genes with top 25% variance → construction of PPI by candidate genes with minimum required interaction score at 0.400, and further screen candidate genes with top 10 degrees in PPI → selection of hub genes by survival and cox analyses in TCGA database
  6. Method 6: Identification of DEGs → screening candidate genes with top 25% variance → construction of PPI by candidate genes with minimum required interaction score at 0.400, and further identification of hub genes bearing top 10 degrees in PPI
  7. Method 7: Identification of DEGs → construction of PPI by candidate genes with minimum required interaction score at 0.400, and further screen candidate genes with top 10 degrees in PPI → selection of hub genes by survival and cox analyses in TCGA database
  8. Method 8: Identification of DEGs → construction of PPI by candidate genes with minimum required interaction score at 0.400, and further identification of hub genes bearing top 10 degrees in PPI
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World Journal of Surgical Oncology

ISSN: 1477-7819

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