Pinned Repositories
ProtTrans
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Best-README-Template
An awesome README template to jumpstart your projects!
markdown_readme
Markdown - you can mark up titles, lists, tables, etc., in a much cleaner, readable and accurate way if you do it with HTML.
ProstT5
Bilingual Language Model for Protein Sequence and Structure
ProtSpace3D
Example code on how to visualize proteins in 3D embedding space
SeqVec
Modelling the Language of Life - Deep Learning Protein Sequences
SETH
ProtT5 (Transformer) embeddings used for residue wise disorder prediction in proteins
tape
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
bio_embeddings
Get protein embeddings from protein sequences
mheinzinger's Repositories
mheinzinger/ProstT5
Bilingual Language Model for Protein Sequence and Structure
mheinzinger/SeqVec
Modelling the Language of Life - Deep Learning Protein Sequences
mheinzinger/ProtSpace3D
Example code on how to visualize proteins in 3D embedding space
mheinzinger/Best-README-Template
An awesome README template to jumpstart your projects!
mheinzinger/markdown_readme
Markdown - you can mark up titles, lists, tables, etc., in a much cleaner, readable and accurate way if you do it with HTML.
mheinzinger/SETH
ProtT5 (Transformer) embeddings used for residue wise disorder prediction in proteins
mheinzinger/tape
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
mheinzinger/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
mheinzinger/EAT
Embedding-based annotation transfer (EAT) uses Euclidean distance between vector representations (embeddings) of proteins to transfer annotations from a set of labeled lookup protein embeddings to query protein embedding.