GraphRT: A graph-based deep learning model for predicting the retention time of peptides
arxiv(2024)
摘要
GraphRT is a graph based deep learning model that predicts the retention time
(RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS)
experiments. Each amino acid is represented as a graph, capturing its atomic
and structural properties through a graph neural network. This enables the
model to understand not just the chemical composition of each amino acid, but
also the intricate relationships between its atoms. The sequential context of
the peptide the order and interaction of amino acids in the sequence is then
encoded using recurrent neural networks. This dual approach of graph based and
sequential modelling allows for a comprehensive understanding of both the
individual characteristics of amino acids and their collective behaviour in a
peptide sequence. GraphRT outperforms all current state of the art models and
can predict retention time for peptides containing unseen modifications.
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