http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Emerging trends in applications and infrastructures for computational biology, bioinformatics, and systems biology
"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"--
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.
[edited by] Quoc Nam Tran, Hamid R. Arabnia.
Includes bibliographical references and index.
Emerging trends in computer science & applied computing