{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gao C"],"funding":["NHGRI NIH HHS","National Institutes of Health","NIH HHS"],"pagination":["btae705"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11751632"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(1)"],"pubmed_abstract":["<h4>Motivation</h4>Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using these types of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multichannel sequential signal.<h4>Results</h4>We developed ConvNet-VAEs, a novel framework that uses one-dimensional (1D) convolutional variational autoencoders "],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Integrating single-cell multimodal epigenomic data using 1D convolutional neural networks."],"pmcid":["PMC11751632"],"funding_grant_id":["R01 HG010883"],"pubmed_authors":["Gao C","Welch JD"],"additional_accession":[]},"is_claimable":false,"name":"Integrating single-cell multimodal epigenomic data using 1D convolutional neural networks.","description":"<h4>Motivation</h4>Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using these types of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type. Our key insight is to model single-cell multimodal epigenome data as a multichannel sequential signal.<h4>Results</h4>We developed ConvNet-VAEs, a novel framework that uses one-dimensional (1D) convolutional variational autoencoders ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2025-04-05T11:34:37.488Z","creation":"2025-04-05T11:34:37.488Z"},"accession":"S-EPMC11751632","cross_references":{"pubmed":["39820306"],"doi":["10.1093/bioinformatics/btae705"]}}