ArticleCommunications biology2020
Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 11 citations in OpenAlex.
- Deep learning in single-molecule imaging and analysis: recent advances and prospects.Chemical science · 2022Review
- Toward high-throughput oligomer detection and classification for early-stage aggregation of amyloidogenic protein.Frontiers in chemistry · 2022Article
- Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network.Communications biology · 2020Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
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Abstract
We propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so neither predefined state number nor pre-labelling were required. With the bidirectional Long Short-Term Memory (biLSTM) networks as the hidden layers, both the past and future context can be used fully to improve the prediction results and can even extract information from the noise distribution. The method was validated with the synthetic dataset and the experimental dataset of monomeric fluorophore Cy5, and then applied to extract the membrane protein interaction dynamics from experimental data successfully.
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Registered trials
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