Case studies of neural network applications in various bioinformatics domains
Prerequisites: Introduction to neural networks and their applications in bioinformatics.
Level: Intermediate.
Objectives: Gain basic knowledge of Generative adversarial networks.
Objectives: Gain basic knowledge of Generative adversarial networks.
Introduction to Neural Networks in Bioinformatics
Overview of neural networks and their relevance to bioinformatics
Neural networks types and their applications in bioinformatics
Case Studies of Neural Network Applications in Bioinformatics A. Gene Expression Analysis
Introduction to gene expression analysis and its importance in bioinformatics
Case study: Using neural networks to identify differentially expressed genes in cancer datasets
Case study: Using neural networks to predict gene expression levels from DNA sequence data
Protein Structure Prediction
Introduction to protein structure prediction and its importance in bioinformatics
Case study: Using neural networks to predict protein secondary structure from amino acid sequence data
Case study: Using neural networks to predict protein tertiary structure from primary sequence data
Drug Discovery and Development
Introduction to the role of bioinformatics in drug discovery and development
Case study: Using neural networks to predict drug-target interactions
Case study: Using neural networks to predict drug toxicity
Other Applications of Neural Networks in Bioinformatics
Case study: Using neural networks to classify microarray data for cancer diagnosis
Case study: Using neural networks to predict protein-protein interactions
Conclusion and Future Directions
Summary of key points and main takeaways
Discussion of future trends and potential applications of neural networks in bioinformatics
Proceed to the next lecture: Ensemble deep learning