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Learning Continuous Potentials from smFRET

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.09.12.507719Source: DataRank Database

Learning Continuous Potentials from smFRET is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 1 time.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: greenTopics (23)IDs (PubMed, OpenAlex)SDGs (1)

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Authors (2)

Steve Pressé ORCID,J. Shepard Bryan ORCID

Live enrichment

PubMed:26207759 OpenAlex:https://openalex.org/W4296130786

National Science Foundation

Grant: 1719537

CAREER: Data-Driven Models for Biological Dynamics

National Institutes of Health

Grant: 5R01GM130745-02

A Bayesian nonparametric approach to superresolved tracking of multiple molecules inside living cells

National Institutes of Health

Grant: 5R01GM134426-02

Theoretical Models of Single Molecule Dynamics from Minimal Photon Numbers

greenLicense: cc-by-nc

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/09/13/2022.09.12.507719.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.09.12.507719

repositoryhttps://doi.org/10.1101/2022.09.12.507719

https://doi.org/10.1016/j.bpj.2022.11.2947

Fields of Study

Advanced Fluorescence Microscopy TechniquesProtein Structure and DynamicsGene Regulatory Network Analysis0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

Förster resonance energy transferSingle-molecule FRETComputer scienceHidden Markov modelInterpolation (computer graphics)Kernel (algebra)Artificial intelligenceInferenceAlgorithmMachine learningTheoretical computer sciencePhysicsMathematicsPhotonsFluorescence Resonance Energy TransferBayes TheoremSingle Molecule Imaging

Sustainable Development Goals

SDG 0: Affordable and clean energy