ArticleFrontiers in genetics2019
A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers for the Prediction and Diagnosis of Fibrostenosing Crohn's Disease: A Systematic Review.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2022Pooled it
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.Interdisciplinary sciences, computational life sciences · 2026Article
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-intestinal Diseases.Biochemical genetics · 2025Article
- Gene expression and agent-based modeling improve precision prognosis in breast cancer.Scientific reports · 2025Article
- Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.Scientific reports · 2024Article
- Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis.BMC cardiovascular disorders · 2024Article
- Article
- Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.BMC bioinformatics · 2024Article
- Article
- Genetic profiling in radiotherapy: a comprehensive review.Frontiers in oncology · 2024Review
- Serum Direct Bilirubin as a Biomarker for Breast Cancer.Breast cancer (Dove Medical Press) · 2024Article
- An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction.Pharmacogenomics and personalized medicine · 2024Article
- Article
- A Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram.Healthcare (Basel, Switzerland) · 2023Article
- A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data.Diagnostics (Basel, Switzerland) · 2023Article
- A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.Frontiers in molecular biosciences · 2023Article
- Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges.Frontiers in pharmacology · 2023Review
- Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques.Journal of inflammation research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic profiles among different breast cancer survivors who received similar treatment may provide clues about the key biological processes involved in the cells and finding the right treatment. More specifically, such profiling may help personalize the treatment based on the patients' gene expression. In this paper, we present a hierarchical machine learning system that predicts the 5-year survivability of the patients who underwent though specific therapy; The classes are built on the combination of two parts that are the survivability information and the given therapy. For the survivability information part, it defines whether the patient survives the 5-years interval or deceased. While the therapy part denotes the therapy has been taken during that interval, which includes hormone therapy, radiotherapy, or surgery, which totally forms six classes. The Model classifies one class vs. the rest at each node, which makes the tree-based model creates five nodes. The model is trained using a set of standard classifiers based on a comprehensive study dataset that includes genomic profiles and clinical information of 347 patients. A combination of feature selection methods and a prediction method are applied on each node to identify the genes that can predict the class at that node, the identified genes for each class may serve as potential biomarkers to the class's treatment for better survivability. The results show that the model identifies the classes with high-performance measurements. An exhaustive analysis based on relevant literature shows that some of the potential biomarkers are strongly related to breast cancer survivability and cancer in general.
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