CONTENTS -- Preface -- APBC 2005 Organization -- Contributed Papers -- S.M. Yiu, P.Y. Chan, T.-W. Lam, W.-K. Sung, H.F. Ting, and P.W.H. Wong. Allowing Mismatches in Anchors for Whole Genome Alignment: Generation and Effectiveness -- 1. Introduction -- 2. The x-Mismatch Anchor and its Effectiveness -- 2.1. The x-Mismatch Anchor -- 2.2. Effectiveness of x-Mismatch Anchors -- 3. The Anchor Generation Algorithms -- 4. Conclusion -- References -- Appendix -- H.N. Chua and W.-K. Sung. A Better Gap Penalty for Pairwise-SVM -- 1 Introduction -- 2 SVM-Pairwise -- 3 Multiple local similarity -- 3.1 Recursive Smith-Waterman -- 3.2 Experimental Setup -- 4 Relaxed Gap Penalty -- 5 Conclusion -- References -- Z.H. Huang, X. Zhou, and D. Song. High Dimensional Indexing for Protein Structure Matching Using Bowties -- 1. Introduction -- 2. Preliminaries and Related Work -- 2.1. Structure and sequence similarities -- 2.2. Methods for comparing of 3 0 protein structures -- 3. Problem Formulation -- 3.1. Vector representation of a protein in 3D space -- 3.2. Bowties -- 3.3. Motifs -- 3.4. Motif Matching -- 3.5. Protein structure matching -- 4. A Database Solution to the Problem -- 5. Indexing and Querying Bowties -- 6. Bowtie Indexing Experiments -- 6.1. Test Data -- 6.2. Queries -- 6.3. Performance Indicators -- 6.4. Experimental Results -- 7. Conclusions and Future Work -- Acknowledgements -- References -- G. Butler, G. Wang, Y. Wang, and L. Zou. A Graph Database with Visual Queries for Genomics -- 1. Introduction -- 1.1. A Genomics Case Study -- 2. Related Work -- 3. A Tour of Our System -- 3.1. System Architecture -- 3.2. Query Formulation -- 3.3. Translation of Queries -- 3.4. Visualization of Query Results -- 4. Conclusion and Future Work -- References.

K.M. Konwar, I.I. Mandoiu, A.C. Russell, and A.A. Shvartsman. Improved Algorithms for Multiplex PCR Primer Set Selection with Amplification Length Constraints -- 1. Introduction -- 2. Notations and Problem Formulation -- 3. The Greedy Algorithm -- 3.1. Implementation details -- 4. Experimental Results -- 5. Conclusions -- References -- Dukka Bahadur K.C., E. Tomita, J. Suzuki, K. Horimoto, and T. Akutsu. Clique Based Algorithms for Protein Threading with Profiles and Constraints -- 1. Introduction -- 2. Problem Formulation -- 3. Algorithms -- 3.1. FTHREAD: An eficient algorithm for threading with strict constraints -- 3.2. NTHREAD: Algorithm for non-strict Constraints -- 3.3. EfJicient maximum cliquending algorithm: WCQprime -- 4. Computational Experiments -- 4.1. Comparison with CLIQUETHREAD -- 4.2. Experiments with non-strict Constraints -- 4.3. Threading accuracy VS number of constraints -- 4.4. Comparison with other methods -- 5. Conclusion and Discussion -- Acknowledgments -- References -- S.-L. Wang, C.-M. Chen, and M.-J. Hwang. Classification of Protein 3D Folds by Hidden Markov Learning on Sequences of Structural Alphabets -- 1. Introduction -- 2. Materials and Methods -- 2.1. Derivation of structural alphabets -- 2.2. Hidden Markov training and fold classification -- 3. Results -- 3.1. Number of alphabets for optimal performance -- 3.2. Comparison with the results of Cootes et al. [19] -- 3.3. Test results at different levels of amino acid sequence identity -- 4. Discussion -- Acknowledgments -- References -- J. Xu, L. Yu, and M. Li. Consensus Fold Recognition by Predicted Model Quality -- 1. Introduction -- 2. SVM Regression -- 3. Feature Extraction -- 4. Experimental Results -- 4.1. Sensitivity -- 4.2. Specificity -- 5. Conclusion and Future Work -- Acknowledgement -- References.

R. Bondugula, O. Duzlevski, and D. Xu. Profiles and Fuzzy K-Nearest Neighbor Algorithms for Protein Secondary Structure Prediction -- 1 Introduction -- 2 Methods and Materials -- 2.1 Membership assignment to the neighbors -- 2.2 The Fuzzy K-Nearest neighbor algorithm -- 2.3 Filtering the output -- 3 Results -- 4 Discussion -- Acknowledgments -- References -- J. Feng, L. Parida, and R. Zhou. Protein Folding Trajectory Analysis using Patterned Clusters -- 1. Introduction -- 2. The Problem Description -- 3. On Patterned Clusters -- 4. Case Study: Folding of ,&hairpin -- 4.1. Simulation Parameters (Step 1) -- 4.2. Discovery Parameters (Steps 2 & 3) -- 4.3. Analysis of Results (Step 4) -- 5. Conclusion & Ongoing Work -- References -- E.W. Xu, D.G. Brown, and P. Kearney. The Use of Functional Domains to Improve Transmembrane Protein Topology Prediction -- 1. Introduction -- 2. Computational Prediction of TM Protein Topology -- 2.1 Features of TM Proteins for in silico Modeling -- 2.2 Hidden Markov Model -- 2.3 Review of Existing HMM Models -- 2.3.1 HMM for topologV prediction (HMMTOP) -- 2.3.2 Transmembrane HMM (TMHMW -- 2.3.3 Current programs do not incorporate functional domains -- 3. Adding Functional Domains to TMHMM to Improve the Prediction Accuracy -- 3.1 Viterbi Algorithm -- 3.2 Method -- 3.3 Defhition of Pattern and Domain Predictors -- 3.4 Selection of Pattern and Domain Predictors -- 4. Experimental Results -- 4.1 Data Sets -- 4.2 Test for the Robustness of AHMM -- 4.3 Sensitivity3 and Specificity4 of TMHMM and AHMM on Helix and Sidedness Prediction -- 5. Discussions and Conclusion -- 5.1 The Value of PH -- 5.2 The Scope of AHMM -- 5.3 Functional Domains and Prediction Accuracy -- Acknowledgments -- References -- J. Guo, Y. Lin, and Z. Sun. A Novel Method for Protein Subcellular Localization: Combining Residue-Couple Model and SVM.

1 Introduction -- 2 Method and database -- 2.1 Database -- 2.2 Classifier and support vector machine -- 2.3 Residue-Couple model -- 2.4 Cross-validation and model selection -- 2.5 Prediction result assessments -- 3 Results -- 3.1 Prediction Accuracy -- 3.2 Prediction result and comparison with other methods -- 3.3 Robustness against errors in the N-terminal sequence -- 4 Discussion and future work -- 5 Webserver and application -- 6 Conclusion -- Acknowledgement -- References -- M. Boden and J. Hawkins. Detecting Residues in Targeting Peptides -- 1. Introduction -- 2. Finding sequential patterns -- 3. Method and simulations -- 3.1. Data sets -- 3.2. Networks -- 3.3. Non-plant proteins -- 3.4. Plant proteins -- 4. Conclusion -- References -- S. Maetschke, M. Towsey, and M. Boden. BLOMAP: An Encoding of Amino Acids Which Improves Signal Peptide Cleavage Site Prediction -- 1. Introduction -- 2. Encodings -- 3. BLOMAP-encoding -- 4. Data and Classifiers -- 5. Results and Discussion -- 6. Conclusion -- References -- K.-H. Liang. Cells In Silico (CIS): A Biomedical Simulation Framework Based on Markov Random Field -- 1. Introduction -- 2. Markov Random Fields and CIS -- 3. Tumor, hypoxia and anglogenesis -- 3.1. CIS Methodology -- 3.1.1. Define key entities as random variables -- 3.1.2. States initialization -- 3.1.3. Define interactions between sites -- 3.1.4. Proceed Simulation -- 3.2. Results and Observations -- 4. Discussions and Conclusions -- References -- Z. Zhang, S. Tang, and S.-K. Ng. Toward Discovering Disease-Specific Gene Networks from Online Literature -- 1. Introduction -- 2. Method -- 2.1. System Architecture -- 2.2. Procedures -- 3. Results -- 3.1. Case Study 1: Huntington Disease -- 3.2. Case Study 2: Amyotrophic Lateral Sclerosi -- 3.3. Analysis -- 4. Conclusion -- References.

H. Matsuno, S.-I. Inouye, Y. Okitsu, Y. Fujii, and S. Miyano. A New Regulatory Interactions Suggested by Simulations for Circadian Genetic Control Mechanism in Mammals -- 1. Introduction -- 2. Mammalian Circadian Genetic Control Mechanism on Biological Facts -- 3. Evaluation of the Present Circadian Gene Regulatory Model by Simulations -- 3.1. Molecular interactions in a mathematical model -- 3.2. HFPN model of mammalian circadian gene regulatory mechanism -- 3.3. Simulation results and their inconsistencies with the biological facts -- 4. A New Hypothesis: PER/CRY complex activates the gene Bmal -- 5. Conclusions -- References -- J. Liu, B. Ma, and M. Li. PRIMA: Peptide Robust Identification from MS/MS Spectra -- 1. Introduction -- 2. Background and related work -- 3. Constructing a linear scoring function -- 3.1. Selecting features -- 3.2. A linear programming formulation for the scoring function -- 4. Experimental results -- 5. Conclusions and future work -- Acknowledgements -- References -- K. Duan, and J.C. Rajapakse. SVM-RFE Peak Selection for Cancer Classification with Mass Spectrometry Data -- 1. Introduction -- 2. SVM, SVM-RFE and T-Statistics -- 2.1. SVM -- 2.2. SVM-RFE -- 2.3. T-Statistics -- 3. Numerical Experiments -- 4. Discussion and Conclusion -- Acknowledgment -- References -- X. Wang and D.D. Feng. Hybrid Registration for Two-Dimensional Gel Protein Images -- 1 Introduction -- 2 Hybrid Registration Algorithm -- 2.1 Global Hierarchical Registration Based on Wavelet Decomposition -- 2.1.1 Wavelet-based image decomposition -- 2.1.2 The Proposed Wavelet-based Hierarchical Registration -- 2.1.3 Registration criterion-mutual information (MI) -- 2.2 Local Elastic Registration Based on Automatically Localized Landmark Points -- 2.2.1 Automatic Landmark Points Localization -- 2.2.2 Thin-Plate Splines (TPS).

3 Experimental Validation and Discussion.