Machine Learning for Biological Discovery
Heinzinger LabWe build machine learning tools for biological discoveries. For example, we train foundation models for molecular biology and apply them for annotating the vast amounts of unlabeled data but also to design and optimize new molecular machines.
We build machine learning tools for biological discoveries. For example, we train foundation models for molecular biology and apply them for annotating the vast amounts of unlabeled data but also to design and optimize new molecular machines.
Our Researchers
Publications
Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Milot Mirdita, Martin Steinegger, Burkhard Rost. Bilingual language model for protein sequence and structure. Nature Communications, 2024.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger et al. ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
Michael Heinzinger, Ahmed Elnaggar, Yu Wang, Christian Dallago, Dmitrii Nechaev, Florian Matthes, Burkhard Rost. Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinformatics, 2019.
Michael Heinzinger, Maria Littmann, Ian Sillitoe, Nicola Bordin, Christine Orengo, Burkhard Rost. Contrastive learning on protein embeddings enlightens midnight zone. NAR Genomics and Bioinformatics, 2022.
Noelia Ferruz, Michael Heinzinger, Mehmet Akdel, Alexander Goncearenco, Luca Naef, Christian Dallago. From sequence to function through structure: Deep learning for protein design. Computational and Structural Biotechnology Journal, 2023.
Robert Schmirler, Michael Heinzinger, Burkhard Rost.Fine-tuning protein language models boosts predictions across diverse tasks. Nature Communications, 2024.
Maria Littmann, Michael Heinzinger, Christian Dallago, Tobias Olenyi, Burkhard Rost. Embeddings from deep learning transfer GO annotations beyond homology. Scientific Reports, 2021.
Céline Marquet, Michael Heinzinger, Tobias Olenyi, Christian Dallago, Kyra Erckert, Michael Bernhofer, Dmitrii Nechaev, Burkhard Rost. Embeddings from protein language models predict conservation and variant effects. Human Genetics, 2022.
Dagmar Ilzhoefer, Michael Heinzinger, Burkhard Rost.SETH predicts nuances of residue disorder from protein embeddings. bioRxiv, 2022.
For the complete list, see Google Scholar
