{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang Q"],"funding":["U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)","NIAID NIH HHS","Silicon Valley Community Foundation (SVCF)","U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)","NIGMS NIH HHS"],"pagination":["1863-1872"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11468773"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(10)"],"pubmed_abstract":["Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of pro"],"journal":["Nature methods"],"pubmed_title":["MiLoPYP: self-supervised molecular pattern mining and particle localization in situ."],"pmcid":["PMC11468773"],"funding_grant_id":["R01 GM141223","U54 AI170752","DAF2021-234602","R01GM141223","U54-AI170752"],"pubmed_authors":["Bartesaghi A","Huang Q","Zhou Y"],"additional_accession":[]},"is_claimable":false,"name":"MiLoPYP: self-supervised molecular pattern mining and particle localization in situ.","description":"Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of pro","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-06-01T07:06:26.011Z","creation":"2025-04-04T23:50:13.265Z"},"accession":"S-EPMC11468773","cross_references":{"pubmed":["39251798"],"doi":["10.1038/s41592-024-02403-6"]}}