This journal publishes research on the application of computational and data-driven methods to understand and analyze cancer. Articles focus on developing and validating machine learning and deep learning models for cancer diagnosis, prognosis, and treatment prediction. The scope also includes the analysis of multi-omics data, such as transcriptomics and epigenomics, to identify molecular subtypes, biomarkers, and therapeutic targets in various cancer types. Investigations into the tumor microenvironment, immune cell infiltration, and the role of specific genes and pathways in cancer progression and response to therapy are also featured.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Cancer ResearchQ3
OncologyQ3
Subject Classification
Web of Science Categories
Mathematical & Computational BiologyOncology
Scopus Categories
Cancer ResearchOncology
Research Topics (OpenAlex)
Gene expression and cancer classificationBioinformatics and Genomic NetworksCancer Genomics and DiagnosticsFerroptosis and cancer prognosisMolecular Biology Techniques and ApplicationsComputational Drug Discovery MethodsAI in cancer detectionRNA modifications and cancerCancer-related molecular mechanisms researchGenomics and Chromatin Dynamics