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01.Gene Silencing: New Research
02.New Research on DNA Damage
03.Life Span Development in Genetic Disorders: Behavioral and Neurobiological Aspects
04.MicroRNA let-7: Role in Human Diseases and Drug Discovery
05.Myelin Basic Protein
06.New Research on Genomic Instability
07.Developmental Gene Expression Regulation
08.Lecture Notes on Computational Mutation
09.Fragile Sites: New Discoveries and Changing Perspectives
10.Progress in Stem Cell Applications
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Lecture Notes on Computational Mutation
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Authors: Guang Wu and Shaomin Yan (Dream Tech SciTech Consulting, Guangdong Province, China) 
Book Description:
The computational mutation is an approach through quantification of DNA/RNA/protein sequence using sophisticated mathematical methods to analyze the mutation trend, simulate the evolutionary process through mutations, and predict the mutations at DNA/RNA/protein levels. Therefore, the computational mutation approach includes two stages of developments, the development of methods for quantification of DNA/RNA/proteins, and the application of mathematical models to quantified DNA/RNA/proteins.
Perhaps, the best way to predict the mutation is to find out the causes, which led to mutations in the past. With such a cause-mutation relationship, the authors can build either quality or quantity models to predict the mutations. Ideally, the authors hope to input a protein/RNA/DNA sequence into the model running in computer, and then we get the model output with the mutation position and would-be-mutated amino acid/RNA code/DNA code.

Table of Contents:
Preface: i-ix

Chapter 1. Measuring Living Protein, pp. 1-3

Chapter 2. Amino-Acid Predictability, pp. 5-15

Chapter 3. Amino-Acid Pair Predictability, pp. 17-48

Chapter 4. Behavior of Amino-acid Pair Predictability, pp. 49-66

Chapter 5. Application of Amino-Acid Pair Predictability, pp. 67-74

Chapter 6. Behavior of Amino-Acid Distribution Probability, pp. 75-94

Chapter 7. Application of Amino-Acid Distribution Probability, pp. 95-110

Chapter 8. Future Amino-Acid Composition, pp. 111-132

Chapter 9. Behavior of Future Amino-Acid Composition and Its Application, pp. 133-148

Chapter 10. Prediction of Mutation Position using Logistic Regression, pp. 149-176

Chapter 11. Prediction of Mutation Position using Neural Network, pp. 177-193

Chapter 12. Future Development, pp. 195-206

Appendix, pp. 207-210

References, pp. 211-221


   Binding: Hardcover
   Pub. Date: 2013- January
   Pages: 230, 7 x 10,
   ISBN: 978-1-60456-516-4
   Status: AV
Status Code Description
AN Announcing
FM Formatting
PP Page Proofs
FP Final Production
EP Editorial Production
PR At Prepress
AP At Press
AV Available
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Lecture Notes on Computational Mutation