Artificial Neural Networks Methods in Molecular Biology 2190 3rd Edition by Hugh Cartwright – Ebook PDF Instant Download/Delivery: 9781071608258 ,1071608258
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Product details:
ISBN 10: 1071608258
ISBN 13: 9781071608258
Author: Hugh Cartwright
This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication, studies of Tuberculosis, gene signatures in breast cancer classification, the use of mass spectrometry in metabolite identification, visual navigation, and computer diagnosis. Written in the highly successful __Methods in Molecular Biology__ series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls.
Artificial Neural Networks Methods in Molecular Biology 2190 3rd Edition Table of contents:
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Chapter 1: Introduction to Artificial Neural Networks
- Overview of Neural Networks in Computational Biology
- Key Concepts: Neurons, Layers, and Activation Functions
- Historical Development of Neural Networks in Science
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Chapter 2: Fundamentals of Neural Networks in Biology
- Biological Inspiration: The Neuron Model
- Types of Neural Networks: Feedforward, Recurrent, Convolutional
- Essential Mathematical Foundations and Algorithms
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Chapter 3: Data Preprocessing for Neural Network Applications
- Importance of Data Cleaning and Normalization
- Feature Extraction and Selection
- Strategies for Handling Biological Data Sets
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Chapter 4: Training Neural Networks for Molecular Biology
- Supervised vs. Unsupervised Learning
- Optimization Techniques: Gradient Descent, Backpropagation
- Regularization Methods and Overfitting Prevention
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Chapter 5: Case Study 1: Protein Structure Prediction
- Using Neural Networks for Predicting Protein Folding
- Data Sets and Network Architecture for Structural Predictions
- Evaluating Model Performance and Accuracy
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Chapter 6: Case Study 2: Gene Expression Data Analysis
- Applying Neural Networks to Gene Expression Profiling
- Identifying Patterns and Predictive Models in Genomic Data
- Challenges and Insights from Gene Expression Studies
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Chapter 7: Case Study 3: Drug Discovery and Design
- Neural Networks in Drug Discovery and Virtual Screening
- Molecular Docking and Predicting Drug-Target Interactions
- Success Stories and Limitations of Neural Networks in Pharma
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Chapter 8: Case Study 4: Systems Biology and Pathway Modeling
- Neural Networks in Modeling Biological Pathways and Networks
- Approaches for Understanding Complex Biological Systems
- Network Inference and Predicting Molecular Interactions
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Chapter 9: Advanced Techniques in Neural Networks for Biology
- Deep Learning in Molecular Biology
- Convolutional Neural Networks (CNNs) for Sequence and Structure Analysis
- Recurrent Neural Networks (RNNs) for Temporal Data in Biological Systems
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Chapter 10: Challenges and Future Directions
- Current Limitations and Obstacles in Neural Network Applications
- Emerging Trends: Transfer Learning, Reinforcement Learning, and AI
- Future Applications in Precision Medicine and Personalized Therapy
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Chapter 11: Neural Networks in Bioinformatics and Genomics
- Integrating Neural Networks with Bioinformatics Tools
- Big Data in Genomics and the Role of Neural Networks
- Applications in Whole-Genome Sequencing and Analysis
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Hugh Cartwright,Artificial Neural Networks,Molecular Biology