{"title":"Effectively detecting anomalous diffusion via deep learning","authors":"Adrian Pacheco-Pozo, Diego Krapf","doi":"10.1038/s43588-024-00705-5","DOIUrl":null,"url":null,"abstract":"A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"731-732"},"PeriodicalIF":12.0000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00705-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.
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