{"doi":"10.3389/fphys.2022.957484","title":"Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms","abstract":"Macromolecular structure classification from cryo-electron tomography (cryo-ET) data is important for understanding macro-molecular dynamics. It has a wide range of applications and is essential in enhancing our knowledge of the sub-cellular environment. However, a major limitation has been insufficient labelled cryo-ET data. In this work, we use Contrastive Self-supervised Learning (CSSL) to improve the previous approaches for macromolecular structure classification from cryo-ET data with limited labels. We first pretrain an encoder with unlabelled data using CSSL and then fine-tune the pretrained weights on the downstream classification task. To this end, we design a cryo-ET domain-specific data-augmentation pipeline. The benefit of augmenting cryo-ET datasets is most prominent when the original dataset is limited in size. Overall, extensive experiments performed on real and simulated cryo-ET data in the semi-supervised learning setting demonstrate the effectiveness of our approach in macromolecular labeling and classification.","journal":"Frontiers in Physiology","year":2022,"id":279340,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9482,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":952911,"name":"Xuehai He","orcid":null,"position":1,"is_corresponding":false},{"id":741562,"name":"Mostofa Rafid Uddin","orcid":null,"position":2,"is_corresponding":false},{"id":383207,"name":"Xiangrui Zeng","orcid":"0000-0002-8589-4474","position":3,"is_corresponding":false},{"id":952453,"name":"Andrew Zhou","orcid":"0000-0001-6656-8123","position":4,"is_corresponding":false},{"id":952454,"name":"Jing Zhang","orcid":"0000-0003-2541-4923","position":5,"is_corresponding":false},{"id":307539,"name":"Zachary Freyberg","orcid":"0000-0001-6460-0118","position":6,"is_corresponding":false},{"id":383213,"name":"Min Xu","orcid":"0000-0002-0881-5891","position":7,"is_corresponding":false},{"id":623363,"name":"Tarun Gupta","orcid":"0000-0002-9235-8717","position":0,"is_corresponding":true}],"reference_count":85,"raw_metadata":null,"created_at":"2026-07-19T00:28:51.322666Z","pmid":"36111160","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}