Screen LibraryEmerging trends in computational biology, bioinformatics, and systems biology : algorithms and software tools /
This book discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simulation techniques. It addresses the development and application of data-analytical and theoret...
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| Corporate Authors: | |
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| Group Author: | ; |
| Published: |
Elsevier : Morgan Kaufman,
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| Publisher Address: | Waltham, MA : |
| Publication Dates: | [2015] |
| Literature type: | eBook |
| Language: | English |
| Series: |
Emerging trends in computer science & applied computing
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| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9780128025086 |
| Summary: |
This book discusses the latest developments in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytics and algorithms, mathematical modeling, and simulation techniques. It addresses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems; presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications; provides a systems biology perspec |
| Carrier Form: | 1 online resource. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9780128026465 0128026464 0128025085 9780128025086 |
| Index Number: | QH324 |
| CLC: | Q811.4 |
| Contents: |
Front Cover; Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools; Copyright; Contents; Contributors; Preface; Acknowledgments; Introduction; Chapter 1: Supervised Learning with the Artificial Neural Networks Algorithm for Modeling Immune Cell Differentiation; 1. Introduction; 1.A. Immune cell differentiation and modeling; 1.B. MSM and model reduction; 1.C. ANN algorithm and its applications; 2. Related work; 3. Modeling immune cell differentiation; 3.1. T cell differentiation process as a use case. 3.2. Data for training and testing models3.3. ANN model; 3.4. Comparative analysis with the LR model and SVM; 3.5. Capability of ANN model to analyze data with noise; 4. Discussion; 5. Conclusion; References; References; Chapter 2: Accelerating Techniques for Particle Filter Implementations on FPGA; 1. Introduction; 2. PF and SLAM algorithms; 2.1. Particle filtering; 2.2. Application of PF to SLAM; 3. Computational bottleneck identification and hardware/software partitioning; 4. PF acceleration techniques; 4.1. CORDIC acceleration technique; 4.2. Ziggurat acceleration technique. 5. Hardware implementation6. Hardware/software Architecture; 7. Results and discussion; 8. Conclusions; References; Chapter 3: Biological Study on Pulsatile Flow of Herschel-Bulkley Fluid in Tapered Blood Vessels; 1. Introduction; 2. Formulation of the problem; 3. Solution; 4. Discussion; 5. Conclusion; References; Chapter 4: Hierarchical k-Means: A Hybrid Clustering Algorithm and Its Application to Study Gene Expression in Lung Adeno ... ; 1. Introduction; 2. Methods; 3. Data Set; 4. Results and Discussion; 5. Conclusions; References; Supplementary Materials. Chapter 5: Molecular Classification of N-Aryloxazolidinone-5-carboxamides as Human Immunodeficiency Virus Protease Inhibitors1. Introduction; 2. Computational method; 3. Classification algorithm; 4. Information entropy; 5. The EC of entropy production; 6. Learning procedure; 7. Calculation results and discussion; 8. Conclusions; Acknowledgment; References; Chapter 6: Review of Recent Protein-Protein Interaction Techniques; 1. Introduction; 2. Technical challenges and open issues; 3. Performance measures; 4. Computational approaches; 4.1. Sequence-based approaches. 4.1.1. Statistical sequence-based approaches4.1.1.1. Mirror tree method; 4.1.1.2. PIPE; 4.1.1.3. CD; 4.1.2. ML sequence-based approaches; 4.1.2.1. Auto covariance; 4.1.2.2. Pairwise similarity; 4.1.2.3. AA composition; 4.1.2.4. AA Triad; 4.1.2.5. UNISPPI; 4.1.2.6. ETB-Viterbi; 4.2. Structure-based approaches; 4.2.1. Template structure-based approaches; 4.2.1.1. PRISM; 4.2.1.2. PrePPI; 4.2.2. Statistical structure-based approaches; 4.2.2.1. PID matrix score; 4.2.2.2. PreSPI; 4.2.2.3. DCC; 4.2.2.4. MEGADOCK; 4.2.2.5. Meta approach; 4.2.3. ML structure-based approaches; 4.2.3.1. Random Forest. |