About Machine learning: life sciences

Machine learning (ML) aims to automate analytical model building to either provide a predictive model to annotated future samples, cluster samples on their feature profile or provide insights into which features are associated with a label. While predominantly applied in consumer or web-analytics applications, there are also successful applications in the life sciences field. This collection is aimed at promoting the applications of ML approaches to life science data with a focus on sharing how a generic algorithm was adapted or how the data was prepared to achieve optimal performance as well as demonstrating good model validation practices.