Bio-medical Ontologies Maintenance and Change Management

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334 M.T. Hoque, M. Chetty, and A. Sattar 0 ≤ n T ≤ 2 . The value of dim in (7) for 2D square and 3D cube HP lattice model H is 1 and 2 respectively (and in (8) for 2D FCC and 3D FCC the values are 2 and 5 respectively). The ‘min’ implies ‘minimum of’. The n H in (8) indicates the total number of hydrophobic residues. Note, the minimum value of both | α ( t) | and | β ( t) | is 1 and so never becomes zero in (4), (5) and (6), thereby preserving the sub-conformation or schema possessing good features, that may have been created in the alternate phase. The search uses a simple GA (SGA) paradigm which is hybridized (see Algorithm 1) with the aforementioned move sets, PCF etc with a population size of 200 for all sequences. The elite rate = 0.1, crossover rate = 0.85, mutation rate = 0.5 and single point mutation by pivot rotation was applied. The implementation of both crossover and mutation operations were as in [2], but without any special treatment such as cooling. The roulette wheel selection procedure was used. Algorithm 1. HGA for PSP Input : Sequence S. Output: Conformation with best fitness, F. COMPUTE: PCF, A. t = 0, F = 0 /* Gen. count and fitness initialization */ Populate with random (valid) conformations based on S. WHILE NOT Terminate Condition { t = t + 1, COMPUTE δ(t), α(t), β(t), TF CROSSOVER and then MUTATION IF δ(t) < 0 THEN { FOR i =1 to population_size DO Check chromosome for any miss mapping of HBBC based on Model. i IF miss-mapping = TRUE THEN {Re-map the sub-sequence to corresponding HBBC using move-sets.}} COMPUTE: TF, SORT, KEEP Elite F � Best fitness found from the population. } END. The experiment results were very impressive (see Table 3) and outperformed in all available low resolution models including square 2D [3], cube 3D [80], 2D FCC lattice [81] and 3D FCC lattice model [82]. This general concept is referred to as guided GA [3, 80] or hybrid GA [12, 81], and it importantly provides a intelligent backtracking capability if any local minimum is assumed. Combining HGA with twin removal (as mentioned in Section 4.1) having r = 0.8, it was shown in [82] to obtain best performance over the form of i) SGA, ii) SGA + r = 0.8 and iii) HGA - for the 3D FCC lattice model. As there could be a number of possible lattice structure or orientations [34], we next justify the preferred on for PSP problem (in Section 5) and modify the two bead HP model further to improve the prediction in Section 6.
Genetic Algorithm in Ab Initio Protein Structure Prediction 335 Table 3. Performance comparison of nondeterministic search approaches [12] using 2D HP square lattice model Length / Sequence 20 / (HP)2PH(HP)2(PH)2HP(PH)2 -9 -9 -9 -9 -9 -9 24 / H2P2HP2HP2(HPP)4H2 -9 -9 -9 -9 -9 -9 25 / P2HP2H2P4H2P4H2P4H2 -8 -8 -8 -8 -8 -8 36 / P3(H2P2)2P2H7P2H2P4H2P2HP2 -14 -14 -14 -12 -13 -14 48 / (P2H)2(HP2)2P4H10P6(H2P2)2HP2H5 -23 -23 -23 -22 -20 -23 50 / H2(PH)3PH4PH(P3H)2P4H(P3H)2PH4P(HP)3H2 -21 -21 -21 -21 -21 -21 60 / P2H3PH8P3H10PHP3H12P4H6PH2PHP -36 -35 -35 -34 -33 -35 64 / 12(PH)2(P2H2)2P2HP2H2PPHP2H2P2(H2P2)2 (HP) 2H12 -42 -39 -39 -37 -35 -40 5 Preferred Lattice Structure for PSP A number of lattice models are used for studying the PSP problem. However, towards preferring a lattice structure or orientation in 3D for effectively mapping the real folded protein, we advocate the preference of the 3D face-centred-cube (FCC) orientation for the following reasons: i) Based on the full proof of Kepler Conjecture [86], a 3D FCC is proven to be the densest sphere packing orientation. It can provide densest protein core [87] while predicting a protein structure (though the protein core may not necessarily need to be in the most compact form [88]). ii) In 3D FCC orientation, a residue can have 12 neighbours in a 3D space [82]. Such orientation allows maximum excluded volume for offering densest compactness [3, 80, 81]. Therefore logically inferring, for a region with fixed volume, an FCC model has more option for placing a residue in suitable neighbouring position with respect to another residue than any other lattice models. A rudimentary example is, the FCC model is parity [88] problem free, whereas the square or the cube lattice is not. iii) Therefore, within the lattice constraints, the FCC lattice can provide maximum degree of freedom and FCC can provide closest resemblance to the real or high resolution folding [3, 75, 80, 81]. In the FCC orientation, if its 12 neighbours are assumed to be covered with a thin outer layer, the overall structure resembles to a cuboctahedron [3, 80, 82] (see the shape of the inner kernel in Fig. 17 (b)), where a cuboctahedron has 14 faces, 6 of them are square and 8 of them are equilateral triangle and it has 12 corners of vertices. GGA [3] GTB [58] EMC [57] GA [2] MC [2] CI [85]
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Bio-medical Ontologies Maintenance and Change Management

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