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Table 6 Representative examples of association rules for each type

From: Prediction of protein-protein interaction types using association rule based classification

#a

Ob

Rule descriptionc

Typed

Confe

Suppf

Cg

Gh

Ki

Uj

Sk

Il

1

3

If 77.31 ≤ Loop < 80.56

ENZ

0.811

0.032

1

0.214

1

1

1

0.722

2

8

If 17.57 ≤ Helix < 20.87

ENZ

0.545

0.032

1

0.102

1

1

1

0.668

3

9

If SCOPClass = 7

ENZ

0.725

0.053

1

0.184

1

1

-

0.660

4

26

If 67.59 ≤ Loop < 70.83

ENZ

0.526

0.032

-

0.048

1

1

1

0.601

5

28

If 461.83 ≤ df-ASA < 681.42 AND 2.3 ≤ LCS < 2.73

ENZ

0.625

0.032

-

0.120

1

1

-

0.555

6

37

If 57.87 ≤ Loop < 61.11

ENZ

0.467

0.037

-

0.045

-

1

1

0.510

7

2

If SCOPClass = 1 AND 12.25 ≤ nFrag < 16 AND NoStrand

nonENZ

0.882

0.032

1

0.250

1

1

1

0.738

8

11

If .66 ≤ inPro < .87

nonENZ

0.597

0.042

1

0.129

1

1

-

0.628

9

15

If 26.74 ≤ nAA < 35.32 AND 901.01 ≤ df-ASA < 1120.6

nonENZ

0.556

0.032

1

0.133

1

1

-

0.620

10

18

If SCOPClass = 1 AND 1.87 <= LCS < 2.3 9

nonENZ

0.545

0.032

1

0.137

1

1

-

0.619

11

20

If 1.43 ≤ LCS < 1.87

nonENZ

0.556

0.042

1

0.074

1

1

-

0.612

12

21

If NoStrand AND 1.87 ≤ LCS < 2.3

nonENZ

0.515

0.037

-

0.113

1

1

1

0.611

13

36

If 58.11 ≤ ASAPR < 59.52

nonENZ

0.476

0.032

1

0.065

-

1

-

0.515

14

38

If 41.67 ≤ Loop < 44.91

nonENZ

0.423

0.032

-

0.046

-

1

1

0.500

15

40

If SCOPClass = 1 AND NoStrand

nonENZ

0.484

0.064

-

0.074

-

 

1

0.406

16

46

If 125.14 ≤ nAtom < 165.52 AND 901.01 ≤ df-ASA < 1120.6

nonENZ

0.412

0.037

-

0.050

-

1

-

0.375

17

64

If .42 ≤ HH < .44

nonENZ

0.347

0.037

-

0.009

-

1

-

0.348

18

5

If 7.78 ≤ Strand < 10.27

HET

0.660

0.037

1

0.141

1

1

1

0.691

19

7

If 2.8 ≤ Strand < 5.29

HET

0.565

0.037

1

0.089

1

1

1

0.670

20

12

If 205.9 ≤ nAtom < 246.28

HET

0.574

0.037

1

0.143

1

1

-

0.626

21

25

If 44.91 ≤ Loop < 48.15

HET

0.479

0.037

1

0.110

-

1

1

0.604

22

32

If 3.6 ≤ LCS < 4.03

HET

0.461

0.037

1

0.100

-

1

-

0.520

23

33

If .44 ≤ HH < .46

HET

0.467

0.045

1

0.070

-

1

-

0.516

24

63

If SCOPClass = 1 AND NoStrand

HET

0.282

0.037

-

0.074

-

-

1

0.348

25

31

If SCOPClass = 3 AND 2.3 ≤ LCS < 2.73

HOM

0.470

0.033

1

0.100

-

1

-

0.521

26

98

If 3.17 ≤ LCS < 3.6

HOM

0.337

0.035

-

0.034

-

-

-

0.135

27

133

If 26.74 ≤ nAA < 35.32

HOM

0.237

0.039

-

0.041

-

-

-

0.106

  1. Representative examples of 27 rules within top 30% are listed by sorting Columns Type and I. Rules of which order is below 48 are added for explaining overlapping rules and the comparison to rules produced from a decision tree.
  2. a#: Rule identifier;
  3. bO: Order of a rule ranking by importance factor;
  4. cRule description: The body of a rule;
  5. dType: The head of a rule representing a PPI type;
  6. eConf: Confidence of a rule;
  7. fSupp: Support of a rule;
  8. gC: Rules selected from correlation-based feature subset selection [32];
  9. hG: The worth of a rule by measuring the gain ratio [33]with respect to PPI types;
  10. iK: Top K rules ranked within top 30%;
  11. jU: Unique rules;
  12. kS: SSE content rules;
  13. lI: Importance factor of a rule calculated by an average of all factors such as Conf, Supp, C, G, K, U and S; "-" is replaced with value 0 when the importance factor was calculated.