HannesStark/protein-localization
Using Transformer protein embeddings with a linear attention mechanism to make SOTA de-novo predictions for the subcellular location of proteins :microscope:
This project helps biologists and biochemists predict where a protein resides within a cell based solely on its amino acid sequence. You input the protein's amino acid sequence, and the system outputs its likely subcellular location, such as cytoplasm, nucleus, or mitochondria. This tool is ideal for researchers studying protein function, drug discovery, or cellular processes.
No commits in the last 6 months.
Use this if you need to rapidly and accurately determine the subcellular location of newly identified proteins or a large set of proteins without experimental validation.
Not ideal if you already have experimental data on protein localization or require predictions that incorporate complex evolutionary profile information beyond single sequences.
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76
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12
Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 06, 2023
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