williamdee1/LMPred_AMP_Prediction
A novel approach to the classification of antimicrobial peptides (AMPs) using pre-trained language models to create contextual vectorized embeddings of each peptide sequence before a convolutional neural network is used as the classifier.
This project helps biological researchers and pharmaceutical developers rapidly identify potential antimicrobial peptides (AMPs) from amino acid sequences. It takes a peptide sequence as input and predicts whether it is an AMP, providing a crucial first step in drug discovery. This is intended for scientists working in drug development, bioinformatics, or biochemistry to efficiently screen new therapeutic candidates.
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Use this if you need to quickly and cost-effectively screen a large number of peptide sequences to identify potential antimicrobial candidates without extensive wet-lab experiments.
Not ideal if you require definitive experimental validation, as this tool provides in-silico predictions that still need laboratory confirmation.
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Jupyter Notebook
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Last pushed
Sep 10, 2024
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