ArticleGenome biology2022
Identifying tumor cells at the single-cell level using machine learning.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant.bioRxiv : the preprint server for biology · 2026Article
- A unified single-cell atlas of HNSCC: Toward characterizing HPV- and sex-associated TME variability.iScience · 2026Article
- Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.Communications biology · 2026Article
- Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning.Genome medicine · 2026Article
- Benchmarking scRNA-seq Copy Number Inference: A Comprehensive Evaluation and Practitioner's Guide.bioRxiv : the preprint server for biology · 2026Article
- Article
- Immune infiltration landscape and potential drug-targeted implications for hepatocellular carcinoma with 'progression/hyper-progression' recurrence.Annals of medicine · 2025Article
- Identification of malignant cells in single-cell transcriptomics data.Communications biology · 2025Review
- CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.BMC biology · 2025Article
- CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer.Genome biology · 2025Article
- Machine learning and single-cell analysis uncover distinctive characteristics of CD300LG within the TNBC immune microenvironment: experimental validation.Clinical and experimental medicine · 2025Article
- Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery.Nature communications · 2025Article
- scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures.Communications biology · 2025Article
- scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.Interdisciplinary sciences, computational life sciences · 2025Article
- Single-cell dissection reveals promotive role of ENO1 in leukemia stem cell self-renewal and chemoresistance in acute myeloid leukemia.Stem cell research & therapy · 2024Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.