Computational Systems Bioinformatics: Conference Proceedings CSB2008 Life Sciences Society

CONTENTS......Page 12
Preface......Page 6
Committees......Page 8
Referees......Page 10
Genomics......Page 16
1. INTRODUCTION......Page 18
2.1. ORFome Assembly Algorithm......Page 20
3. RESULTS......Page 22
3.2. Homology Search of Assembled Peptides......Page 23
3.3. Novel Assignments of Functional Categories by Assembled Peptides......Page 24
4. DISCUSSION......Page 26
References......Page 27
1. INTRODUCTION......Page 30
2.1. Quantum probabilistic coding......Page 31
2.3. Objective function......Page 32
3.2. Genetic operators......Page 33
3.2.2. Global mutation operators......Page 34
3.2.3. The Selection operator......Page 35
4.1. QGMALIGN algorithm......Page 36
5.1. The experimental results......Page 37
5.2. Comparisons and analysis......Page 39
References......Page 40
1. INTRODUCTION......Page 42
2. RELATED WORK......Page 43
3.3. Path-Counting Formula......Page 44
4.2. Path-Counting Formula for abc......Page 45
5.3. Calculating abc F......Page 47
5.4. Computing aab F and aaa F......Page 48
6.2. Experimental Results......Page 49
Acknowledgement......Page 50
References......Page 51
1. INTRODUCTION......Page 52
2. PROBABILITY MODELS......Page 53
3.2. Selecting One Voting Algorithm......Page 54
3.3. Selecting k Voting Algorithm......Page 57
3.4. Progressive Filtering Algorithm......Page 58
3.5. Motif Refinement......Page 59
4. EXPERIMENTS......Page 60
References......Page 61
Proteomics......Page 64
1. INTRODUCTION......Page 66
2.1. Data sources......Page 68
2.2. An outline of GFA......Page 69
2.4.1. The simple strategy......Page 70
3.1. Some useful de.nitions and notations......Page 71
3.2. Matching to the benchmark......Page 72
3.3. Comparison with the previous methods......Page 74
4. CONCLUSIONS AND DISCUSSION......Page 75
References......Page 76
Appendix: additional .gures and tables......Page 77
1. INTRODUCTION......Page 78
3.1. Spectrum Preparation......Page 80
3.2. Thumbnail of a Spectrum and Rapid Filtration......Page 81
4. THE DATABASE SYSTEM......Page 83
5. EXPERIMENTS RESULT......Page 84
References......Page 85
1. INTRODUCTION......Page 88
2.1. Similarity between Genes Based on Gene Ontology Data......Page 90
2.2. Mixture Model and Parameter Estimation......Page 91
2.3. Function Prediction......Page 92
3.1.1. Gene Ontology......Page 93
3.2. Results on Using the Mixture Model......Page 94
3.3. Function Prediction......Page 97
References......Page 98
1. INTRODUCTION......Page 100
2.1. Gabor Filters......Page 101
2.2. GaborLocal Method......Page 102
2.2.1. Full frequency MS signal generation......Page 103
2.2.3. Peak quantification by maximum rank......Page 105
3.2. CWT Method......Page 107
3.3. Evaluation Using ROC Curve......Page 108
4. CONCLUSION......Page 109
References......Page 111
Structural Bioinformatics......Page 112
1. INTRODUCTION......Page 114
2.1. Bayes Error Bounds......Page 115
2.2. Planning Algorithm......Page 117
3. RESULTS......Page 119
Acknowledgments......Page 122
References......Page 123
1. INTRODUCTION......Page 124
2.1. General Algorithmic Concepts......Page 125
2.2. Introduction of Double-Center “Stars”......Page 126
2.3.2. Learning from others......Page 128
3.2. Building up Stars from Local Alignments......Page 129
3.4. The Feedback Procedure......Page 130
4.2. Experimental Results......Page 132
4.3. Discussion on the Results......Page 133
References......Page 135
1. INTRODUCTION......Page 136
2.2. Support vector machines and kernel methods......Page 138
2.3.2. Mismatch kernel......Page 139
2.4.1. Amino acid and dipeptide composition......Page 140
2.4.3. Composition-Transition-Distribution......Page 141
3. RESULTS AND DISCUSSION......Page 142
3.1. Web server......Page 143
4. SUMMARY AND DISCUSSION......Page 145
References......Page 146
1. INTRODUCTION......Page 148
2. BACKGROUND......Page 149
3.1. Spatial sample neighborhood kernels......Page 150
4.3. Comparison on Ding and Dubchak benchmark......Page 151
5. DISCUSSION......Page 152
5.2. Comparison of the features induced by different string kernels......Page 155
6. CONCLUSIONS......Page 156
References......Page 157
1. INTRODUCTION......Page 160
2. CMS AND SSPS......Page 161
2.2. Secondary structure profiles......Page 162
3.1. Selecting candidate SSPs......Page 163
3.2.1. Computing sensitivity and FP rate......Page 164
4.1. SSP utility for structured RNAs......Page 165
4.2. Evaluation on Rfam database......Page 166
4.2.1. Sensitivity and FP rate......Page 167
4.3. Comparison with other filters......Page 168
4.4. Analysis of low-sensitivity SSPs......Page 169
References......Page 170
1. INTRODUCTION......Page 172
2. RELATED WORK......Page 173
3.1. Preprocessing Stage......Page 175
3.2. Matching Stage......Page 176
4.2. Matching Results......Page 177
5. CONCLUSION AND FUTURE WORK......Page 180
References......Page 181
1. INTRODUCTION......Page 184
2. PRELIMINARIES AND PROBLEM DEFINITION......Page 186
3. PREVIOUS WORK......Page 187
4.2. Protein backbone structure determination from residual dipolar couplings......Page 188
4.3. NOE pattern matching based on the Hausdor  distance measure......Page 189
5.1. Analysis of rotamer selection based on NOE patterns......Page 190
6.1. Robustness of Hausdor  distance and NOE assignment accuracy......Page 191
6.2. Evaluation of structures from our NOE assignment tables......Page 192
References......Page 194
1.1. Dynamic Programming Approach......Page 198
1.3. Our Contributions......Page 199
2.2.3. Finding an Alignment......Page 200
2.3. Scoring the Alignments......Page 201
3.1. RIPC Set......Page 202
3.2. Measuring Sensitivity and Specificity Using CATH......Page 203
3.2.2. Alignment Results......Page 204
3.2.3. Running Times......Page 205
3.4. Two Non-sequential Alignments......Page 206
4. DISCUSSION......Page 207
References......Page 208
1. INTRODUCTION......Page 210
2.1. Feature Inputs and Data Sets......Page 211
2.3. Profile Feature Set and Integration of the Two Feature Sets......Page 212
3. RESULTS......Page 213
References......Page 216
1. INTRODUCTION......Page 218
3.1. Capability of traversing a \"rough\" energy landscape......Page 219
3.2. Results for the shortest domains......Page 220
3.3. Insights into the protein folding process......Page 221
4. DISCUSSION......Page 222
References......Page 223
1. INTRODUCTION......Page 226
Author Index......Page 14
3.1. Primary structure and sequence data......Page 227
3.4. Homology modeling data......Page 228
4.2. Predicted secondary structure......Page 229
4.3.2. Modified alignments: using binding labels......Page 230
4.6.2. Homology modeling quality......Page 231
5.1. Ligand-bind residue prediction from sequence and structure......Page 232
5.2.4. Comparisons......Page 233
5.2.5. Generalization of model parameters......Page 234
6. CONCLUSIONS......Page 235
7. ACKNOWLEDGMENTS, SUPPLEMENTS......Page 236
Pathways, Networks, and Biological Systems......Page 238
1. INTRODUCTION......Page 240
2. WORKING EXAMPLE......Page 241
3.1. Inferring Relations Between Phenotypes......Page 242
3.2.2. Ranking Phenotypes for a Gene......Page 243
4.2. Experiment Setup......Page 244
4.3. Results......Page 245
4.3.3. Predicting Phenotypes to Genes Without Any Interactions......Page 246
4.4. Phenotype Relations......Page 247
5. DISCUSSION......Page 248
References......Page 250
1. INTRODUCTION......Page 252
3. MODEL......Page 254
4. ALGORITHM......Page 255
4.1. Pairwise Similarity of Entities......Page 256
4.2. Similarity of Topologies......Page 257
4.3. Combining Homology and Topology......Page 258
4.5. Similarity Score of Pathways......Page 259
5.1. Biological Signi.cance......Page 260
5.3. Running Time......Page 262
References......Page 263
1. INTRODUCTION......Page 264
2.2. Identifying modules correlated with clinical parameters......Page 266
2.6. Network and expression data......Page 267
3.1. Breast cancer dataset......Page 268
3.2. Comparison with other methods......Page 271
References......Page 272
Computational Genomics......Page 274
1. INTRODUCTION......Page 276
1.2. Outline......Page 277
2.2. Genome halving......Page 278
4. THE GGH ALGORITHM......Page 279
4.1. The algorithms......Page 280
5. RESULTS AND DISCUSSION......Page 284
References......Page 285
1. INTRODUCTION......Page 288
1.4. Errors in Microsatellite Data......Page 289
2. CONSENSUS BASED APPROACH FOR ERROR-TOLERANT SIBLINGS RECONSTRUCTION......Page 290
2.2. Distance-based Consensus......Page 291
3.2. Greedy Algorithm......Page 293
4.1. Random Simulations......Page 294
4.4. Sibling Group Reconstruction Methods......Page 295
6. CONCLUSIONS......Page 296
References......Page 298
1.1. Related works......Page 300
1.2. Our contribution......Page 301
3. RESOLVING ORDERING CONFLICTS......Page 302
3.1. An LP-based algorithm......Page 303
3.2. A speed-up heuristic......Page 305
5. LINEARIZING THE DAG......Page 306
6.1. Evaluation of the conflict-resolution......Page 307
6.2. Comparison with JOINMAP......Page 308
References......Page 310
1. INTRODUCTION......Page 312
2.1. Mendelian constraints as a linear system......Page 314
2.3. Linear constraints on h variables......Page 315
3.1. Split nodes to break cycles......Page 316
3.2. Detect path constraints from locus graphs......Page 317
3.3. Encode path constraints in disjoint-set structure D......Page 318
4.2. Pedigrees with missing data......Page 319
5. EXPERIMENTAL RESULTS......Page 320
6. DISCUSSION......Page 322
References......Page 323
Computational Methods......Page 324
1. INTRODUCTION......Page 326
4. EXPERIMENTAL RESULTS AND DISCUSSION......Page 327
References......Page 329
1. INTRODUCTION......Page 330
2.1. Dynamic Time Warping......Page 332
2.2. Multisegment Time Series Alignment......Page 333
2.4. Experiments......Page 334
3. THE CONE HEURISTIC......Page 335
3.2. Experiments......Page 336
4. THE HYBRID-DTW HEURISTIC......Page 338
4.2. Experiments......Page 339
5. DISCUSSION......Page 340
References......Page 341
1. INTRODUCTION......Page 342
2. RELATED WORK......Page 343
3.2. Graph Kernel Functions......Page 344
4.1. Graph Alignment Kernel......Page 345
4.3. Graph Wavelet Analysis......Page 346
4.3.2. Discrete Wavelet Functions......Page 347
5. EXPERIMENTAL STUDY......Page 348
5.2. Methods......Page 349
5.3.2. Computational Efficiency......Page 350
6. CONCLUSIONS......Page 351
References......Page 352
Author Index......Page 354