reverse engineering – recent advances and applications - OpenLibra

More documents

Recommendations

Info

228 Reverse Engineering – Recent Advances and Applications �r1,1 � �r2,1 � � r � � 1,2 where �1 �c � � r � 2,2 and 1 � � � � 2 �c � � 2� � � � � � � �� r1,D�� r2,D�� At any instant, each particle is aware of its individual best position, wibest , () t , as well as the � best position of the entire swarm, wGbest , () t . The parameters c1 and c2 are constants that weight particle movement in the direction of the individual best positions and global best positions, respectively; and r1,j and r2,j, j � 1,2, � D are random scalars distributed uniformly between 0 and 1, providing the main stochastic component of the PSO algorithm. Figure 7 shows a vector diagram of the contributing terms of the PSO trajectory update. The � new change in position, �wi( t�1) , is the resultant of three contributing vectors: (i) the inertial component, �wi() t � � , (ii) the movement in the direction of individual best, wibest , () t , � and (iii) the movement in the direction of the global (or neighborhood) best, w , () t . The constriction factor, � , may also help to ensure convergence of the PSO algorithm, and is set according to the weights c1 and c2 as in Eq.(6). 2 � � , � �c1 �c2, � � 4 2 2�� 4� The key strength of the PSO algorithm is the interaction among particles. The second term in � � Eq. (5), �2 ( wGbest , ( t) �wi( t)) , is considered to be a “social influence” term. While this term � � tends to pull the particle towards the globally best solution, the first term, �1 ( wibest , ( t) � wi( t)) , allows each particle to think for itself. The net combination is an algorithm with excellent trade-off between total swarm convergence, and each particle’s capability for global exploration. Moreover, the relative contribution of the two terms is weighted stochastically. The algorithm consists of repeated application of the velocity and position update rules presented above. Termination occurs by specification of a minimum error criterion, maximum number of iterations, or alternately when the position change of each particle is sufficiently small as to assume that each particle has converged. Fig. 7. Vector diagram of particle trajectory update. A pseudo-code description of the PSO algorithm is provided below: Gbest (6)
Reverse Engineering Gene Regulatory Networks by Integrating Multi-Source Biological Data 1. Generate initial population of particles, i w� , i=1,2,…,n, distributed randomly (uniform) 2. within the specified bounds. Evaluate each particle with the objective function, f ( wi ) � ; if any particles have located new individual best positions, then replace previous individual best positions, wibest , � , and keep track of the swarm global best position, wGbest , � 3. . � Determine new trajectories, �wi( t�1) , according to Eq. (5). 4. 5. Update each particle position according to Eq. (4). � Determine if any wi( t�1) are outside of the specified bounds; hold positions of particles within the specified bounds. If termination criterion is met (for example completed maximum number of iterations), then w � is the best solution found; otherwise, go to step (2). Gbest , Selection of appropriate values for the free parameters of PSO plays an important role in the algorithm’s performance. In our study, the parameters setting is presented in Table 1, and the constriction factor χ is determined by Eq.(6). The PSOt Toolbox (Birge 2003) was used for implementation of PSO. Parameter Value Maximum velocity, Vmax 2 Maximum search space range, |Wmax| [-5,5] Acceleration constants, c1 & c2 2.05, 2.05 Size of Swarm 20 Table 1. PSO parameter setting 3.3.4 GA-PSO training algorithm A hybrid of GA and PSO methods (GA-PSO) is applied to determine the gene modules that may be regulated by each transcription factor. Genetic algorithm generates candidate gene modules, while the PSO algorithm determines the parameters of a given NN represented by a weight vector . The RMSE between the NN output and the measured expression profile is returned to GA as a fitness function and to guide the selection of target genes through reproduction, cross-over, and mutation over hundreds of generations. The stopping criteria are pre-specified minimum RMSE or maximum number of generations. The GA-PSO algorithm is run for each transcription factor to train a NN that has the architecture mimicking the identified known NM(s) for the transcription factor. Thus, for a given transcription factor (input), the following steps are carried out to identify its likely downstream gene modules (output) based on their NM(s): 1. Assign the NM to the transcription factor it belongs to. 2. Use the following GA-PSO algorithm to build a NN model that mimics the NM to identify the downstream gene modules. a. Generate combinations of M gene modules to represent the target genes that may be regulated by the transcription factor. Each combination is a vector/chromosome. The initial set of combinations is composed of the initial population of chromosomes. b. Use the PSO algorithm to train a NN model for each chromosome, where the input is the transcription factor and the outputs are gene modules. The goal is to 229
Page 1 and 2:
REVERSE ENGINEERING - RECENT ADVANC
Page 5 and 6:
Contents Preface IX Part 1 Software
Page 9 and 10:
Preface Introduction In the recent
Page 11 and 12:
Preface XI and con’s related to t
Page 13 and 14:
Preface XIII the step-by-step appli
Page 17:
Part 1 Software Reverse Engineering
Page 20 and 21:
4 Reverse Engineering - Recent Adva
Page 22 and 23:
Page 24 and 25:
Page 26 and 27:
10 Reverse Engineering - Recent Adv
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
58 2. Model driven architecture: An
Page 76 and 77:
Page 78 and 79:
62 Fig. 1. Model, metamodels and tr
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
100 Reverse Engineering - Recent Ad
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
108 Table 1. Number of smoothers an
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 133 and 134:
1. Introduction 60 Surface Reconstr
Page 135 and 136:
Surface Reconstruction from Unorgan
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
1. Introduction A Systematic Approa
Page 151 and 152:
A Systematic Approach for Geometric
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
A Review on Shape Engineering and D
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194: A Review on Shape Engineering and D
Page 203 and 204: Integrating Reverse Engineering and
Page 231: Part 3 Reverse Engineering in Medic
Page 234 and 235: 218 Reverse Engineering - Recent Ad
Page 254 and 255: 238 5. Summary and discussions Reve
Page 284 and 285: 268 Fig. 6. A closed polygon mesh r
Page 288 and 289: 272 3. Results Reverse Engineering
Page 292: 276 Reverse Engineering - Recent Ad
show all

reverse engineering – recent advances and applications - OpenLibra

Create successful ePaper yourself

Delete template?

Save as template?