Includes bibliographical references and index.

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"Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology 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 simu- lation techniques"-- Provided by publisher.

Front Cover; Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: System ... ; Copyright ; Contents; List of Contributors; Preface; Introduction; Acknowledgments; Section I: Computational Biology -- Methodologies and Algorithms; Chapter 1: Using Methylation Patterns for Reconstructing Cell Division Dynamics: Assessing Validation Experiments; 1.1. Introduction; 1.1.1. Using Methylation Patterns; 1.1.2. Bisulfite Treatment; 1.2. Errors, Biases, and Uncertainty in Bisulfite Sequencing; 1.3. Model for Degradation and Sampling.

1.3.1. Modeling1.3.2. Simulation Study: Effects of Degradation; 1.4. Statistical Inference Method; 1.5. Simulation Study: Bayesian Inference; 1.6. Discussion; 1.6.1. Different Experiments; 1.6.2. Opportunities; 1.6.3. Conclusions; References; Chapter 2: A Directional Cellular Dynamic Under the Control of a Diffusing Energy for Tissue Morphogenesis: Phenotype and ...; 2.1. Introduction; 2.2. Mathematical Morphological Dynamics; 2.2.1. Gene and Status Expression; 2.3. Attainable Sets of Phenotypes; 2.3.1. Implementation.

2.4. Prediction Tool Based on a Coevolution of a Dynamic Tissue with an Energy Diffusion2.4.1. Prediction of Tissue Growth; 2.4.2. Energy Diffusion Model; 2.4.2.1. Mitosis; 2.4.2.2. Quiescence; 2.4.2.3. Apoptosis; 2.4.3. Results; 2.5. Discussion; References; Chapter 3: A Feature Learning Framework for Histology Images Classification; 3.1. Introduction; 3.2. Methods; 3.2.1. Color and Color Spaces; 3.2.2. Features Extraction and Classification; 3.3. Proposed System; 3.4. Image Data Sets; 3.5. Experimental Results; 3.6. Conclusion; References.

Chapter 4: Spontaneous Activity Characterization in Spiking Neural Systems with Log-Normal Synaptic Weight Distribution4.1. Introduction; 4.2. Models of Spontaneous Activity; 4.3. Model and Methods; 4.3.1. LIF Neural System Applied Synaptic Input; 4.3.2. Izhikevich Neural System Used for Synaptic Input; 4.3.3. Evaluation Indices; 4.4. Results and Evaluations; 4.4.1. Effect of Input Spike From Weak Synapse in LIF Neural System; 4.4.2. Spike Transmission in LIF Neural System; 4.4.3. Spike Transmission in Izhikevich Neural System; 4.5. Conclusions; References.

Chapter 5:Comparison Between OpenMP and Mpich Optimized Parallel Implementations of a Cellular Automaton that Simulates th ...5.1. Introduction; 5.1.1. The Cellular Automaton Game of Life; 5.2. MPICH Optimized Approach of the Cellular Automaton; 5.2.1. MPI Standard; 5.2.2. Description of the MPICH Approach of the Cellular Automaton; 5.2.3. MPICH Implementation of the Cellular Automaton; Code 1. Program code of the MPICH version of Game of Life; 5.3. OpenMP Optimized Approach of the Cellular Automaton; 5.3.1. Open Multiprocessing.