Pathway modeling and algorithm research / Nikos E. Mastorakis, editor.

Format:

Book

Contributor:

Mastorakis, Nikos E.

Series:

Computer Science, Technology and Applications

Computer science, technology and applications

Language:

English

Subjects (All):

Fault-tolerant computing.

Mobile computing.

Algorithms.

Physical Description:

1 online resource (193 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2011.

Language Note:

English

Summary:

Presents and discusses research in the study of computer science. This title includes topics such as: biological pathways; supervised learning approaches; pathway modelling; shortest path algorithms; clustering algorithms; and, inductive logic programming and computationally efficient approximation schemes for functional optimisation.

Contents:

Intro

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

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

Chapter 3 SHORTEST PATH ALGORITHMS IN PATHWAY ANALYSIS

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

Chapter 4 CLUSTERING ALGORITHMS IN PATHWAY MODELING

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

4.3. Application to Gene Regulatory Network Modeling.

5. Current Research

Chapter 5 PATHWAY MODELING: NEW FACE OF GRAPHICAL PROBABILISTIC ANALYSIS

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

4.3. Application to Gene Regulatory Networks

Chapter 6 INDUCTIVE LOGIC PROGRAMMING IN PATHWAY ANALYSIS

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

Chapter 7 GRAPHICS ALGORITHMS UNDER HIGH PERFORMANCE RECONFIGURABLE SYSTEMS

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

Chapter8COMPUTATIONALLYEFFICIENTAPPROXIMATIONSCHEMESFORFUNCTIONALOPTIMIZATION

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

INDEX

Blank Page.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-61209-475-9

OCLC:

744633858