PATHWAY MODELING AND ALGORITHM RESEARCH -- PATHWAY MODELING AND ALGORITHM RESEARCH -- CONTENTS -- PREFACE -- Chapter 1 BIOLOGICAL PATHWAYS AND THEIR MODELING -- Abstract -- 1. Introduction -- 2. Classification -- 2.1. Gene Regulatory Networks (GRN) -- 2.2. Signaling Pathways (SP) -- 2.3. Metabolic Pathways (MP) -- 3. Modeling Biological Pathways -- 4. Current Research -- Conclusions -- References -- Chapter 2 SUPERVISED LEARNING APPROACHES IN PATHWAY MODELING -- Abstract -- 1. Introduction -- 2. Classification via Supervised Learning -- 2.1. Various Approaches -- 2.1.1. Artificial Neural Networks (ANN) -- 2.1.1.1. Application to Metabolic Pathway Modeling -- 2.1.1.2. Application to Signal Transduction Modeling -- 2.1.1.3. Application to Gene Regulatory Network Modeling -- 2.1.2. Support Vector Machines (SVM) -- 2.1.2.1. Application to Metabolic Pathway Modeling -- 2.1.2.2. Application to Gene Regulatory Network Modeling -- 2.1.2.3. Application to Signal Transduction Modeling -- 2.1.3. Nearest Neighbor Approach -- 2.1.3.1. Application to Metabolic Pathway Modeling -- 2.1.3.2. Application to Gene Regulatory Network Modeling -- 2.1.3.3. Application to Signal Transduction Modeling -- 2.1.4. Bayesian Classifier -- 2.1.4.1. Application to Metabolic Pathway Modeling -- 2.1.4.2. Application to Gene Regulatory Network Modeling -- 2.1.4.3. Application to Signal Transduction Modeling -- 2.1.5. Logistic Regression -- 2.1.5.1. Application to Metabolic Pathway Modeling -- 2.1.5.2. Application to Gene Regulatory Network Modeling -- 2.1.5.3. Application to Signal Transduction Modeling -- 2.1.6. Discriminant Analysis -- 2.1.6.1. Application to Metabolic Pathway Modeling -- 2.1.6.2. Application to Gene Regulatory Network Modeling -- 2.1.6.3. Application to Signal Transduction Modeling -- 2.1.7. Decision Trees.

2.1.7.1. Application to Metabolic Pathway Modeling -- 2.1.7.2. Application to Gene Regulatory Network Modeling -- 2.1.7.3. Application to Signal Transduction Modeling -- 3. Current Research -- Conclusions -- References -- Chapter 3 SHORTEST PATH ALGORITHMS IN PATHWAY ANALYSIS -- Abstract -- 1. Introduction -- 2. Shortest Path Algorithms -- 3. Types of Shortest Path Algorithms -- 3.1. Dijkstra's Algorithm -- 3.1.1. Algorithm -- 3.1.2. Pseudocode -- 3.1.3. Time Complexity -- 3.2. Bellman-Ford Algorithm -- 3.2.1. Algorithm -- 3.2.2. Pseudocode -- 3.2.3. Time Complexity -- 3.3. Floyd-Warshall algorithm -- 3.3.1. Algorithm -- 3.3.2. Pseudocode -- 3.3.3. Time Complexity -- 3.4. Johnson's Algorithm -- 3.4.1. Algorithm -- 3.4.2. Pseudocode -- 3.4.3. Time Complexity -- 3.5. Breadth First Search (BFS) -- 3.5.1. Algorithm -- 3.5.2. Pseudocode -- 3.5.3. Time Complexity -- 3.6. k-Shortest Paths -- 3.6.1. Algorithm -- 3.6.2. Pseudo-Code -- 3.6.2.1. Removing Path Algorithm -- 3.6.2.2. Deviation Path Algorithm -- 3.6.3. Time Complexity -- 3.7. Linear Programming -- 3.7.1. Algorithm -- 3.7.2. Pseudo-Code -- 3.7.3. Time Complexity -- 4. Application of Shortest Path Algorithms in Biological Pathways -- 4.1. Application to Metabolic Pathway Modeling -- 4.2. Application to Signal Transduction Modeling -- 4.3. Application to Gene Regulatory Network Modeling -- 5. Current Research -- Conclusion -- References -- Chapter 4 CLUSTERING ALGORITHMS IN PATHWAY MODELING -- Abstract -- 1. Introduction -- 2. Clustering Algorithms -- 3. Types of Clustering Algorithms -- 3.1. Hierarchical Clustering -- 3.2. Partition Clustering -- 3.3. Mixture Models -- 4. Application of Clustering Algorithms in Biological Pathways -- 4.1. Application to Metabolic Pathway Modeling -- 4.2. Application to Signal Transduction Modeling -- 4.3. Application to Gene Regulatory Network Modeling.

5. Current Research -- Conclusion -- References -- Chapter 5 PATHWAY MODELING: NEW FACE OF GRAPHICAL PROBABILISTIC ANALYSIS -- Abstract -- 1. Introduction -- 2. Graphical Probabilistic Models -- 3. Types of Graphical Probabilistic Models -- 3.1. Bayesian Networks -- 3.2. Gaussian Networks -- 3.3. Maximum Likelihood -- 3.4. Density Estimation -- 3.5. Helmholtz Machine (HM) -- 3.6. Latent Variable Models (LVM) -- 3.7. Generative Topographic Mapping (GTM) -- 3.8. Hidden Markov Model (HMM) -- 4. Application of Graphical Probabilistic Models -- 4.1. Application to Metabolic Pathway Modeling -- 4.2. Application to Signal Transduction Modeling -- 4.3. Application to Gene Regulatory Networks -- 5. Current Research -- Conclusion -- References -- Chapter 6 INDUCTIVE LOGIC PROGRAMMING IN PATHWAY ANALYSIS -- Abstract -- 1. Introduction -- 2. Inductive Logic Programming -- 3. Types of Inductive Logic Programming -- 3.1. Probabilistic Inductive Logic Programming -- 3.2. Collaborative Inductive Logic Programming -- 3.3. Generic Rough Set Inductive Logic Programming -- 3.4 Constraint Inductive Logic Programming -- 3.5. Support vector Inductive Logic Programming -- 3.6. Low Size-Complexity Inductive Logic Programming -- 3.7. Non-monotonic Inductive Logic Programming -- 4. Application of Inductive Logic Programming in Biological Pathways -- 4.1. Application to Metabolic Pathway Modeling -- 4.2. Application to Signal Transduction Modeling -- 4.3. Application to Gene Regulatory Network Modeling -- 5. Current Research -- Conclusion -- References -- Chapter 7 GRAPHICS ALGORITHMS UNDER HIGH PERFORMANCE RECONFIGURABLE SYSTEMS -- Abstract -- 1. Introduction -- 2. Reconfigurable Computing Systems -- 2.1. History of Reconfigurable Computing -- 2.2. Field Programmable Gate Arrays -- 2.3. Reconfigurable Systems Generalities -- 2.3.1. Granularity.

2.3.2. Depth of Programmability -- 2.3.3. Reconfigurability -- 2.3.4. Interface Coupling -- 2.4. Reconfigurable Systems -- 2.5. Application of Reconfigurable Systems -- 2.5.1. Information Coding -- 2.5.2. Space and Solar Applications -- 2.5.3. Digital Signal Processing -- 2.5.4. Digital Image Processing -- 2.5.5. Biomedical Engineering -- 2.5.6. Networking -- 2.5.7. Security -- 2.6. High-Level Reconfigurable Hardware Development -- 2.7. Future of RC-Systems at a Glance -- 3. The MorphoSys -- 3.1. The Core Processor -- 3.2. The Reconfigurable Cell -- 3.3. The Reconfigurable Cell Array -- 3.4 The Context Memory -- 3.5. The Frame Buffer and the DMA Controller -- 3.6. The MorphoSys Execution Flow Model -- 3.7. Important Features of MorphoSys -- 4. Graphics Geometrical Transformations under MorphoSys -- 4.1. Geometrical Transformations -- 4.1.1. Translations -- 4.1.2. Scaling -- 4.1.3. Rotation and Shearing -- 4.2. Mapping Translation and Scaling in Basic Forms -- 4.2.1. Translation Using Vector-vector Operations -- 4.2.2. Scaling Using Vector-scalar Operations -- 4.3. Basic Transformation Compositions -- 4.3.1. Composition of Two Translations -- 4.3.2. Composition of Two Scaling Transformations -- 4.3.3. Composition of Translation and Scaling -- 4.4. Transformations Using the General Matrix Form -- 4.4.1. First Mapping -- 4.4.1. Second Mapping -- 4.5. Performance Evaluation and Analysis -- Conclusion -- References -- Chapter8COMPUTATIONALLYEFFICIENTAPPROXIMATIONSCHEMESFORFUNCTIONALOPTIMIZATION -- Abstract -- 1.Introduction -- 2.TheoreticalIssues -- 2.1.Fixed-BasisversusVariable-BasisApproximationSchemes -- 2.2.AccuracyofSuboptimalSolutionsbyVariable-BasisApproximationSchemes -- 2.3.SummingUp -- 3.AnExample:OptimalFaultDiagnosis -- 3.1.StatementofanOptimalDiagnosisProblem -- 3.2.ReductiontoaNonlinearProgrammingProblembyVariable-BasisApproximationSchemes.

3.3.OptimizationoftheParametersintheVariable-BasisFunctions -- 4.CaseStudyandNumericalResults -- References -- INDEX -- Blank Page.