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Appendix 1 E-Predict Algorithm |
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Require: t, R, b, nt, λ |
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1: ∏ = ∅ |
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2: for each protein p ∈ R do |
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3: if p·fold ∉ ∏ then |
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4: ∏ = ∏ ∪ {p·fold} |
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5: Count[p·fold] ← 1 |
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6: else |
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7: Count[p·fold] ← Count[p·fold] + 1 |
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8: end if |
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9: end for |
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10: for i ← 0 to |∏| - 1 do |
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11: if Count[i] <nt then |
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12: ∏ ← ∏ - {i} |
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13: end if |
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14: [i] ← 0 |
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15: Count[i] ← 0 |
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16: end for |
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17: for each candidate SCOP fold F∈ ∏ do |
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18: for each p ∈ R starting from the top ranked protein do |
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19: if p·fold = F then |
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20: Count[F] ← Count[F] + 1 |
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21: if Count[F] <nt then |
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22: [F] ← [F] + E_Measure(p, b) |
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23: end if |
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24: end if |
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25: end for |
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26: end for |
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27: F* ← arg minf [f] |
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28: if (λ = on) AND (S(t,P0) < S(t, )) then |
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29: F* ← P0.fold |
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30: end if |
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31: return F* |
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Chi et al. BMC Bioinformatics 2006 7:362 doi:10.1186/1471-2105-7-362 |