An Overview of Machine Learning in Biology

There is a long history between Machine Learning and its application in Biology. Machine Learning has allowed many advancements in the field of Biology. This includes studies for prediction and discovery by different Machine Learning techniques for specified types of biological data. The versatility of techniques and frameworks has helped improve Machine Learning every day, and hopefully, this will help us improve and become more efficient on new things we discover and grow our database on biological data. In this paper, we will discuss the different applications of Machine Learning in Biology, i.e., Synthetic Gene Circuits, Convolutional Neural Networks, Recurrent Neural Networks and Generative Adversarial Networks. As the biological and medical fields have been quickly growing into a data-rich environment, Machine Learning has become a vital tool to sort through all of this data. The application of Machine Learning helps in two main facets. The first way is to help classify and predict tasks that a machine can quickly do. Secondly, it does not allow much human input, which will help minimize human bias or performance issues. Neural Networks go hand-in-hand with Deep Learning as they are one of its techniques. By utilizing Deep Learning and Neural Networks, we can expand our learning in Biology far more than ever before.