Prof. Dr. Mario Stanke

A major interest of my group are probabilistic models and machine learning methods for structured biological data and efficient algorithms for inference in these models. In particular we are developing, training and implementing recurrent neural networks,  conditional random fields for different concrete applications such as gene prediction and multiple sequence alignments. Research topics include in particlular the gene finding suites AUGUSTUS and BRAKER. For gene prediction services and more information refer to our Bioinformatics Web Server.

Publications