{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mohammad S"],"funding":["Elsa U. Pardee Foundation","National Institutes of Health","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["126-131"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10529635"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["677"],"pubmed_abstract":["Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial "],"journal":["Biochemical and biophysical research communications"],"pubmed_title":["Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population."],"pmcid":["PMC10529635"],"funding_grant_id":["R15 GM140448","1R15GM140448"],"pubmed_authors":["Amar K","Mohammad S","Chowdhury F"],"additional_accession":[]},"is_claimable":false,"name":"Hybrid AI models allow label-free identification and classification of pancreatic tumor repopulating cell population.","description":"Human pancreatic cancer cell lines harbor a small population of tumor repopulating cells (TRCs). Soft 3D fibrin gel allows efficient selection and growth of these tumorigenic TRCs. However, rapid and high-throughput identification and classification of pancreatic TRCs remain technically challenging. Here, we developed deep learning (DL) models paired with machine learning (ML) models to readily identify and classify 3D fibrin gel-selected TRCs into sub-types. Using four different human pancreatic cell lines, namely, MIA PaCa-2, PANC-1, CFPAC-1, and HPAF-II, we classified 3 main sub-types to be present within the TRC population. Our best model was an Inception-v3 convolutional neural network (CNN) used as a feature extractor paired with a Support Vector Machine (SVM) classifier with radial ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Oct","modification":"2025-04-04T02:19:51.378Z","creation":"2025-04-04T02:19:51.378Z"},"accession":"S-EPMC10529635","cross_references":{"pubmed":["37573767"],"doi":["10.1016/j.bbrc.2023.08.015"]}}