Today, with the advancement of technology and the emergence of various measuring tools, a significant amount of data is accumulated in various fields, including biology and medicine. Due to the huge volume and high dimensions of these data, it is practically impossible to examine and process them using traditional methods, which has led to development of fields such as bioinformatics. One of the approaches used in bioinformatics for studying these data is deep learning. Deep learning is a kind of machine learning that uses deep artificial neural networks. By the increase in the processing power of computer systems in the last two decades, deep learning has attracted a lot of attention and now is applied in various fields. Among the application of deep learning in bioinformatics we can mention the predicting the structure and function of proteins, biomedical imaging, biomedical signal processing, drug design and genomics analysis.
In this workshop, we will review some basic concepts in deep learning then discuss several deep ANN architectures and their application in bioinformatics.
- Introduction to AI and Machine learning
- Basic concepts in Deep Learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Deep Belief Neural Networks (DBN)
- Generative Adversarial Network (GAN)
- Graph Neural Networks (GNN)