Exploring Few-Shot Learning Approaches for Bioinformatics Advancements

Abstract

This chapter delves deeply into few-shot learning, a rapidly developing area crucial in driving bioinformatics forward. First, the basics of few-shot learning are laid forth, emphasizing the field's applicability to bioinformatics. Case studies showcasing real-world applications in areas as varied as protein structure prediction, drug development, and genomic analysis provide a deep dive into several few-shot learning approaches like meta-learning and transfer learning. The chapter also provides an in-depth analysis of recent developments, highlights current difficulties, and suggests exciting new avenues for exploration. This chapter highlights the rising significance of few-shot learning in bioinformatics and provides insights into its potential to benefit biomedical research.