{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yu A"],"funding":["NLM NIH HHS","NCI NIH HHS"],"pagination":["eabi4757"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8932661"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(11)"],"pubmed_abstract":["Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset,"],"journal":["Science advances"],"pubmed_title":["Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI."],"pmcid":["PMC8932661"],"funding_grant_id":["T15 LM007033","U54 CA209971"],"pubmed_authors":["Li Y","Li I","Taylor J","Trope WL","Yu A","Plevritis SK","Yeh C","Shrager J","Ozawa MG","Chiou AE"],"additional_accession":[]},"is_claimable":false,"name":"Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI.","description":"Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset,","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2026-07-14T14:50:05.233Z","creation":"2025-04-04T03:20:20.314Z"},"accession":"S-EPMC8932661","cross_references":{"pubmed":["35302849"],"doi":["10.1126/sciadv.abi4757"]}}