Deep learning based local feature classification to automatically identify single molecule fluorescence events.

ABSTRACT: Long-term single-molecule fluorescence measurements are widely used powerful tools to study the conformational dynamics of biomolecules in real time to further elucidate their conformational dynamics. Typically, thousands or even more single-molecule traces are analyzed to provide statistically meaningful information, which is labor-intensive and can introduce user bias. Recently, several deep-learning models have been developed to automatically classify single-molecule traces. In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patter

SUBMITTER: Zhou S 

PROVIDER: S-EPMC11519536 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature