ArticleJournal of personalized medicine2021
Breast Cancer Type Classification Using Machine Learning.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 241 citations in OpenAlex.
- Artificial intelligence in breast ultrasound: a systematic review of research advances.Frontiers in oncology · 2025Pooled it
- Effect of Neural Tissue Mobilization on Sensory-Motor Impairments in Breast Cancer Survivors with Lymphedema: An Experimental Study.Asian Pacific journal of cancer prevention : APJCP · 2023Trial
- Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers.Molecular oncology · 2026Article
- B.R.E.A.S.T. Breast canceR Enhanced AI-Supported Therapy: A New Interpretable Proteomics-Driven Machine Learning Framework for Therapy Response Prediction in Breast Cancer.International journal of molecular sciences · 2026Article
- Impact of surgical approach and survival prediction of malignant phyllode tumor by machine learning.Updates in surgery · 2026Article
- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- Construction of an ultrasound feature-based diagnostic model for predicting triple-negative breast cancer using 108 machine learning algorithm combinations.Journal of clinical imaging science · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.Frontiers in bioinformatics · 2026Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Machine learning approach to identify significant genes and classify cancer types from RNA-seq data.Global medical genetics · 2025Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.Scientific reports · 2025Article
- Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.Cureus · 2025Review
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics.Biomolecules · 2025Review
- Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients.Breast cancer (Dove Medical Press) · 2025Article
- Prevalence and Predictors of Triple-Negative Breast Cancer Among Sudanese Women: A Retrospective Analysis of Newly Diagnosed Cases.International journal of women's health · 2025Article
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
backgroundBreast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data.
methodsWe performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets.
resultsAmong the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated.
conclusionsThe prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.