ErdösRényi Topology



ARACNE

Relevance Networks

DPI Sensitivity

DPI Precision

Bayesian Networks

Num samples

N
_{
TP
}

N
_{
FP
}

N
_{
TP
}

N
_{
FP
}
  
N
_{
TP
}

N
_{
FP
}

1000

128.00

1.33

143.33

462.67

99.71%

96.78%

50.00

32.33

750

124.33

2.67

139.33

411.00

99.35%

96.46%

45.33

31.00

500

119.00

1.67

130.67

311.33

99.46%

96.37%

41.00

29.00

250

101.00

4.67

110.00

182.33

97.44%

95.18%

24.67

25.33

125

81.00

4.67

84.67

95.00

95.09%

96.10%

5.33

19.00

ScaleFree Topology


ARACNE

Relevance Networks

DPI Sensitivity

DPI Precision

Bayesian Networks

Num samples

N
_{
TP
}

N
_{
FP
}

N
_{
TP
}

N
_{
FP
}
  
N
_{
TP
}

N
_{
FP
}

1000

97.67

2.33

113.33

234.00

99.00%

93.67%

38.67

17.00

750

90.67

3.33

103.00

200.00

98.33%

94.10%

33.33

15.33

500

80.33

5.33

91.67

154.67

96.55%

92.95%

27.00

13.33

250

63.33

7.67

70.00

80.00

90.42%

91.56%

9.00

9.67

125

46.33

3.67

48.00

49.67

92.62%

96.50%

4.00

6.00

 Recovery for varying numbers of samples generated from the Mendes networks, which contain an average of ~194 true interactions after selfloops and bidirectional edges are eliminated. For all sample sizes ARACNE efficiently eliminates almost all false candidate interactions inferred by RNs, as indicated by the DPI sensitivity (calculated as the percent of false positives eliminated by the DPI), with minimal reduction in true positives, as indicated by the DPI precision (calculated as the percent of false positives removed out of the total number of edges removed by the DPI). Moreover, as the sample size decreases, the number of true connections inferred by ARACNE decays gracefully while the number of false positives remains very low, whereas the performance of Bayesian Networks degrades rapidly for smaller sample sizes as the conditional probability tables become very sparsely populated. Results are calculated using a pvalue of 10^{4} for ARACNE and Relevance Networks, yielding <0.5 expected false positives for 4,950 potential interactions, and using a Dirichlet prior with equivalent sample size of one for Bayesian Networks [19]. Results are averaged over three network configurations for each topology.