{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang S"],"funding":["Guizhou Provincial People's Hospital National Science Foundation","Guizhou Provincial People's Hospital Doctor Foundation","Guizhou Provincial Science and Technology Projects","National Natural Science Foundation of China","General Project of Chongqing Natural Science Foundation","Guizhou Provincial People's Hospital Youth Fund"],"pagination":["e18296"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11069052"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["28(9)"],"pubmed_abstract":["We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagn"],"journal":["Journal of cellular and molecular medicine"],"pubmed_title":["Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing."],"pmcid":["PMC11069052"],"funding_grant_id":["GPPH-NSFC-2019-09","82260533","[2018]03","CSTB2023NSCQ-MSX0749","[2018]06","82360482","[2020]1Z066","GPPH-NSFC-2019-18","GPPH-NSFC-D-2019-17","GZSYQN202202","82360376"],"pubmed_authors":["Liu J","Hu Y","Ran Z","Deng M","Hao Y","Tan Y","Han G","Ma J","Xiang X","Wang X","Yang S","Shi H","Luo T"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.","description":"We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagn","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2026-07-14T18:58:47.895Z","creation":"2026-06-22T03:13:04.671Z"},"accession":"S-EPMC11069052","cross_references":{"pubmed":["38702954"],"doi":["10.1111/jcmm.18296"]}}