Michael Heinzinger

The paper improves the purity of protein functional families by clustering them using sequence embeddings, doubling the number of pure clusters per family.

PredictProtein provides protein sequence analysis tools, offering structure and function predictions with updates including deep learning and DNA/RNA/protein binding predictions.

This paper proposes using deep learning embeddings to transfer GO annotations based on protein sequence proximity rather than homology, achieving performance comparable to existing methods.

The paper introduces a lightweight deep learning method using protein language model embeddings to predict protein subcellular localization, outperforming state-of-the-art methods without using multiple sequence alignments.

This paper introduces an end-to-end multitask learning approach using language models to predict protein structure and function without evolutionary alignments, achieving faster predictions with moderate accuracy.

This paper introduces SeqVec, a method to represent protein sequences as continuous vectors using deep learning, achieving better prediction results than methods relying on evolutionary information.