ArticleComputational and structural biotechnology journal2022
Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches.International journal of molecular sciences · 2024Pooled it
- Tumor-infiltrating lymphocytes display prognostic signatures associated with chemotherapy response in TNBC patients.iScience · 2026Article
- Prognostic biomarkers for lung adenocarcinoma based on Mendelian randomization analysis.The Journal of international medical research · 2026Article
- Biomarker identification of triple negative breast cancer subtypes using machine learning.NPJ systems biology and applications · 2026Article
- Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.European journal of cancer (Oxford, England : 1990) · 2026Article
- Gene Expression Profiling to Unveil Novel Biomarkers for Early Diagnosis and Therapies for Breast Cancer.Current topics in medicinal chemistry · 2026Article
- A time-dependent model for quantifying postoperative inflammatory trajectories and predicting survival in early triple-negative breast cancer.World journal of surgical oncology · 2025Article
- Stemness-based gastric cancer classification by machine learning for precision diagnosis and treatment of gastric cancer.Journal of gastrointestinal oncology · 2025Article
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation.Cell regeneration (London, England) · 2025Article
- Machine Learning-Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer.Cancer research and treatment · 2025Article
- Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.Scientific reports · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.BMC medical informatics and decision making · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Article
- Identifying SLC2A6 as the novel protective factor in breast cancer by TP53-related genes affecting M1 macrophage infiltration.Apoptosis : an international journal on programmed cell death · 2024Article
- Evaluation of lncRNAs as Potential Biomarkers for Diagnosis of Metastatic Triple-Negative Breast Cancer through Bioinformatics and Machine Learning.Iranian journal of biotechnology · 2024Article
- A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions.Heliyon · 2024Article
- Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.BMC bioinformatics · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets,
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