ReviewInternational journal of molecular sciences2025
Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.
Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- [Paeoniflorin alleviates cancer-related fatigue during chemotherapy for breast cancer by targeting EZH2/CCNE1 to regulate cell cycle and inflammatory microenvironment].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Identification of key genes as diagnostic biomarkers for lung adenocarcinoma using bioinformatics and machine learning.Discover oncology · 2026Article
- Combined effects of ageing and type 2 diabetes mellitus on human dental mechanics.GeroScience · 2026Article
- Identification and experimental validation of prognostic genes associated with dietary restriction-related signatures in bladder cancer.Frontiers in immunology · 2026Article
- CMA-associated cellular state remodeling defines prognostic heterogeneity and identifies ARRDC3 as a protective functional gene in small cell lung cancer.Frontiers in cell and developmental biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Integrating the advantages of machine learning with the rapidly accumulating high-throughput sequencing data facilitates our capacity for biological discovery and the advancement of molecular medicine. In recent years, bulk RNA-seq technology has established itself as a cost-effective and widely used method for obtaining complete transcriptome profiles of test samples, enabling the identification of key cancer-associated expression patterns. Various machine learning algorithms, in turn, enable the development of informative diagnostic and prognostic models, ensuring the efficient processing of high-dimensional RNA-Seq data. The convergence of these methods shows great promise for oncology. In this narrative review, we describe bulk RNA-Seq-based ML models in oncology as a complete workflow from data preprocessing to model validation. We provide practical recommendations for algorithm selection and study design, and discuss bulk RNA-Seq deconvolution as a cost-effective alternative to single-cell RNA-Seq for analyzing tumor cellular composition. These insights offer a practical guide for developing reproducible diagnostic and prognostic models with translational potential.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.