ArticleBMC genomics2023
A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.
Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- DeepSGE: predicting spatial gene expression using residual network with efficient channel attention and dynamic graph attention network.BMC genomics · 2026Article
- Cancer stem cell heterogeneity‑inspired therapeutics for enhancing tumor treatment outcomes (Review).International journal of oncology · 2026Review
- Immune-related genes can accurately predict survival in bladder cancer: a retrospective study via two independent immunotherapy cohorts.Translational andrology and urology · 2025Article
- Proteomic-based stemness score measures oncogenic dedifferentiation and enables the identification of druggable targets.Cell genomics · 2025Article
- Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression.International journal of general medicine · 2025Article
- Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes.BMC cancer · 2024Article
- Spatial Multiplexing and Omics.Nature reviews. Methods primers · 2024Article
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Authors and funding
17 authors.
Funding
Abstract
Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.
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Registered trials
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