Applications of deep learning in biology

The rapid growth of biological data sets has created a pressing need for powerful compu- tational tools to analyze and interpret this data. Deep learning, a subfield of artificial intelli- gence, has emerged as a promising approach for tackling this challenge. In this thesis, the applications of deep learning in biology, mainly focussing on image analysis and predicting protein-protein interaction are explored. I describe the basic components of deep learning models, including convolutional neural networks and modern attention-based networks, and discuss their potential for analyzing complex biological data sets. I also examine the chal- lenges of integrating and applying deep learning in a biomedical context, including issues of data quality, model interpretability, and ethical considerations. Overall, this thesis highlights the exciting potential of deep learning to transform the field of biological research and provides a roadmap for future applications in this rapidly evolving field.