ArticleMolecules (Basel, Switzerland)2019
Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Activated glucocorticoid receptor is an estrogen receptor silencer in ER+ metastatic breast cancer.EMBO molecular medicine · 2026Article
- Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.Cancers · 2024Article
- Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study.Frontiers in oncology · 2022Article
- Co-Adjuvant Therapy Efficacy of Catechin and Procyanidin B2 with Docetaxel on Hormone-Related Cancers In Vitro.International journal of molecular sciences · 2021Article
- Screening for Core Genes Related to Pathogenesis of Alzheimer's Disease.Frontiers in cell and developmental biology · 2021Article
- Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.Frontiers in genetics · 2020Article
- Integrating Somatic Mutations for Breast Cancer Survival Prediction Using Machine Learning Methods.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase; only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and
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