Cancer Cell Research is an international, peer-reviewed journal dedicated to publishing high-quality research at the intersection of artificial intelligence (AI) and cancer cell biology. The journal aims to accelerate mechanistic discovery and translational innovation by promoting rigorous AI methodologies that generate biologically interpretable and clinically relevant insights into cancer cell states, behaviors and therapeutic vulnerabilities. The journal welcomes studies that develop, validate, or apply AI and advanced computational approaches to understand cancer at the cellular and microenvironmental levels, including tumor heterogeneity, plasticity, cel-cell interactions, evolution, and treatment response. We emphasize reproducibility, transparent reporting, robust validation, encourage the sharing of code, data and benchmarks when feasible.
Original Research, Methods, Resources (Datasets/Benchmarks/Software), Brief Reports, Reviews, and Perspectives.
AI/ML methods for cancer cell research: Machine learning, deep learning and generative models for cancer biology Interpretable, causal and trustworthy AI (uncertainty, robustness, bias mitigation) Graph and network learning for signaling, regulatory and interaction networks Multimodal learning and foundation models for oncology
Single-cell and spatial AI: Single-cell multi-omics (e.g., scRNA-seq, scATAC-seq) and integrative analysis Spatial transcriptomics/proteomics and tissue microenvironment mapping Cell state discovery, lineage/trajectory inference, and cell–cell communication modeling
AI-enabled cellular imaging: Computational pathology and digital histopathology at cellular resolution Live-cell imaging, high-content screening, and phenotypic profiling Segmentation, tracking, representation learning, and image-based biomarker discovery
Perturbation biology and drug discovery: CRISPR screening analytics, Perturb-seq, and functional genomics with AI Drug response prediction, resistance modeling, and synergy discovery AI-guided experimental design and optimization (e.g., active learning)
Cancer cell mechanisms illuminated by AI: Heterogeneity, clonal evolution, genome instability, and stress adaptation Cell death programs, metabolism, senescence, dormancy, invasion/metastasis Immuno-oncology and immune evasion at single-cell and spatial scales
Open Access Policy and Article Processing Charge (APC)
Authors are encouraged to submit well-formatted manuscripts written in clear English. Additionally, many national and private research funding organizations, as well as universities, may cover the APCs for articles resulting from funded research projects.
If your institution is partnered with us you can benefit from full or partial support for article processing charges (APCs) on manuscripts you submit.