Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/207922
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dc.contributor.authorHu, Huabin-
dc.contributor.authorTjaden, Amelie-
dc.contributor.authorKnapp, Stefan-
dc.contributor.authorAntolin, Albert A.-
dc.contributor.authorMüller, Susanne-
dc.date.accessioned2024-02-22T10:18:03Z-
dc.date.available2024-10-04T05:10:08Z-
dc.date.issued2023-12-21-
dc.identifier.issn2451-9448-
dc.identifier.urihttps://hdl.handle.net/2445/207922-
dc.description.abstractDrug-induced phospholipidosis (DIPL), characterized by excessive accumulation of phospholipids in lysosomes, can lead to clinical adverse effects. It may also alter phenotypic responses in functional studies using chemical probes. Therefore, robust methods are needed to predict and quantify phospholipidosis (PL) early in drug discovery and in chemical probe characterization. Here, we present a versatile high-content live-cell imaging approach, which was used to evaluate a chemogenomic and a lysosomal modulation library. We trained and evaluated several machine learning models using the most comprehensive set of publicly available compounds and interpreted the best model using SHapley Additive exPlanations (SHAP). Analysis of high-quality chemical probes extracted from the Chemical Probes Portal using our algorithm revealed that closely related molecules, such as chemical probes and their matched negative controls can differ in their ability to induce PL, highlighting the importance of identifying PL for robust target validation in chemical biology.ca
dc.format.extent65 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.publisherElsevier BVca
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.chembiol.2023.09.003-
dc.relation.ispartofCell Chemical Biology, 2023, vol. 30, num. 12, p. 1634-1651-
dc.relation.urihttps://doi.org/10.1016/j.chembiol.2023.09.003-
dc.rightsccby-nc-nd (c) Elsevier-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceArticles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))-
dc.subject.classificationLipoïdosi-
dc.subject.classificationAprenentatge automàtic-
dc.subject.otherLipidoses-
dc.subject.otherMachine learning-
dc.titleA machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosisca
dc.typeinfo:eu-repo/semantics/articleca
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.date.updated2024-02-19T09:44:57Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.pmid37797617-
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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