ReviewBioengineering (Basel, Switzerland)2023
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
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
85 citing papers in PubMed, 1 synthesis or guideline pooled it, 211 citations in OpenAlex.
- Pooled it
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- Early Cancer Detection: What's Going on and What's Next.MedComm · 2026Review
- Neural Latent Filtering for Gene Discovery in Breast Cancer Subtypes.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- JAK3 identified as a key toxicological target of aristolochic acid in clear cell renal cell carcinoma.Molecular diversity · 2026Article
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Integrating miRNA profiling and machine learning for improved thyroid cancer diagnosis.Frontiers in oncology · 2026Article
- Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression.Evolutionary bioinformatics online · 2026Article
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Stage analysis of gastric cancer: a bioinformatic approach.Gastroenterology and hepatology from bed to bench · 2026Article
- GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification andFrontiers in immunology · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers.Current stem cell research & therapy · 2026Review
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Multi-omics classification of acute myeloid leukemia guides drug combinations to overcome Venetoclax resistance.Cancer drug resistance (Alhambra, Calif.) · 2026Article
- Machine Learning Models for Cancer Research: A Narrative Review of Bulk RNA-Seq Applications.International journal of molecular sciences · 2025Review
- Predicting the Regulatory Dynamics of AML Disease Progression from Longitudinal Multi-Modal Clinical Data.Journal of medical systems · 2025Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.
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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.