Transfer learning and fine-tuning neural networks for bioinformatics

Introduction to Transfer Learning

What is transfer learning?

Why is transfer learning useful for bioinformatics?

Types of transfer learning

Pre-trained Models for Bioinformatics

Overview of pre-trained models for bioinformatics

Using pre-trained models for feature extraction

Using pre-trained models for fine-tuning

Fine-tuning Pre-trained Models for Bioinformatics

Overview of fine-tuning pre-trained models

Steps for fine-tuning a pre-trained model

Techniques for improving fine-tuning results

Transfer Learning for Sequence Analysis

Overview of transfer learning for sequence analysis

Using pre-trained models for sequence classification

Using pre-trained models for sequence labeling

Transfer Learning for Structure Analysis

Overview of transfer learning for structure analysis

Using pre-trained models for protein classification

Using pre-trained models for protein-ligand binding prediction

Transfer Learning for Gene Expression Analysis

Overview of transfer learning for gene expression analysis

Using pre-trained models for gene expression classification

Using pre-trained models for gene expression regression

Transfer Learning for Image Analysis

Overview of transfer learning for image analysis

Using pre-trained models for image classification

Using pre-trained models for object detection

Transfer Learning for Text Analysis

Overview of transfer learning for text analysis

Using pre-trained models for text classification

Using pre-trained models for text generation

Best Practices for Transfer Learning in Bioinformatics

Tips for choosing the suitable pre-trained model

Tips for fine-tuning pre-trained models

Tips for evaluating transfer learning results

Proceed to the next lecture: Evaluating and interpreting the performance of neural networks in bioinformatics

References