LNCS 2810 Similarity Based Neural Networks for Applications in Computational Molecular Biology 1ST EDITON BY Igor Fischer ISBN 9783540408130

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ISBN 10:
ISBN 13: 9783540408130
Author: Igor Fischer

This paper presents an alternative to distance-based neural networks. A distance measure is the underlying property on which many neural models rely, for example self-organizing maps or neural gas. However, a distance measure implies some requirements on the data which are not always easy to satisfy in practice. This paper shows that a weaker measure, the similarity measure, is sufficient in many cases. As an example, similarity-based networks for strings are presented. Although a metric can also be defined on strings, similarity is the established measure in string-intensive research, like computational molecular biology. Similarity-based neural networks process data based on the same criteria as other tools for analyzing DNA or amino-acid sequences.

LNCS 2810  Similarity Based Neural Networks for Applications in Computational Molecular Biology 1ST EDITON Table of contents: 

• Introduction

  • 1.1 Overview of Computational Molecular Biology
  • 1.2 The Role of Neural Networks in Biology
  • 1.3 The Importance of Similarity Measures in Biological Data
  • 1.4 Objectives of the Book
  • 1.5 Structure of the Book

• Foundations of Neural Networks

  • 2.1 Basic Concepts of Artificial Neural Networks
  • 2.2 Types of Neural Networks
  • 2.3 Training Neural Networks: Algorithms and Techniques
  • 2.4 Evaluation Metrics for Neural Networks

• Similarity Measures in Molecular Biology

  • 3.1 Understanding Molecular Similarity
  • 3.2 Traditional Similarity Measures in Biology (e.g., sequence alignment)
  • 3.3 Machine Learning Approaches to Similarity
  • 3.4 Metrics for Molecular Comparison (e.g., Euclidean, Cosine Similarity)

• Similarity-Based Neural Networks

  • 4.1 Concept of Similarity-Based Neural Networks (SBNNs)
  • 4.2 How Similarity-Based Models Work in Computational Biology
  • 4.3 Case Studies in Similarity-Based Learning
  • 4.4 Application Areas of SBNNs in Biology

• Applications of Similarity-Based Neural Networks

  • 5.1 Protein Structure Prediction
  • 5.2 Drug Discovery and Virtual Screening
  • 5.3 Genomic Data Classification
  • 5.4 Biomarker Discovery and Disease Diagnosis
  • 5.5 Modeling Evolutionary Relationships

• Advanced Topics in Neural Networks for Molecular Biology

  • 6.1 Deep Learning Architectures in Molecular Biology
  • 6.2 Transfer Learning and Pre-trained Models for Biological Data
  • 6.3 Multi-Task Learning for Bioinformatics Problems
  • 6.4 Interpretable AI in Computational Biology

• Case Studies and Real-World Applications

  • 7.1 Case Study 1: Similarity Networks for Gene Expression Analysis
  • 7.2 Case Study 2: Neural Networks in Protein-Protein Interaction Prediction
  • 7.3 Case Study 3: Predicting Drug-Target Interactions Using Neural Networks
  • 7.4 Case Study 4: Application in Systems Biology

• Challenges and Future Directions

  • 8.1 Data Challenges in Molecular Biology (e.g., data scarcity, quality)
  • 8.2 Computational Challenges in Scaling Models for Large Datasets
  • 8.3 Ethical Considerations in Using AI for Molecular Biology
  • 8.4 Future Trends in Neural Networks for Bioinformatics

• Conclusion

  • 9.1 Summary of Key Findings
  • 9.2 Contributions of Similarity-Based Neural Networks to Computational Biology
  • 9.3 Outlook on Future Research and Innovations

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