ArticleBioData mining2016
Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer.Breast cancer research : BCR · 2024Article
- ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification.Nucleic acids research · 2023Article
- A human breast atlas integrating single-cell proteomics and transcriptomics.Developmental cell · 2022Article
- Article
- An integrative deep learning framework for classifying molecular subtypes of breast cancer.Computational and structural biotechnology journal · 2020Article
- PCA-PAM50 improves consistency between breast cancer intrinsic and clinical subtyping reclassifying a subset of luminal A tumors as luminal B.Scientific reports · 2019Article
- Identification of the copy number variant biomarkers for breast cancer subtypes.Molecular genetics and genomics : MGG · 2019Article
- Data-Driven Tree Transforms and Metrics.IEEE transactions on signal and information processing over networks · 2018Article
- The Necessity of Data Mining in Clinical Emergency Medicine; A Narrative Review of the Current Literatrue.Bulletin of emergency and trauma · 2017Review
- Life as an early career researcher: interview with Heloisa Helena Milioli.Future science OA · 2016Article
- Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers.PloS one · 2016Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMulti-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subtypes. Towards the development of robust strategies, we designed an iterative approach to consistently discriminate intrinsic subtypes and improve class prediction in the METABRIC dataset.
findingsIn this study, we employed the CM1 score to identify the most discriminative probes for each group, and an ensemble learning technique to assess the ability of these probes on assigning subtype labels using 24 different classifiers. Our analysis is comprised of an iterative computation of these methods and statistical measures performed on a set of over 2000 samples. The refined labels assigned using this iterative approach revealed to be more consistent and in better agreement with clinicopathological markers and patients' overall survival than those originally provided by the PAM50 method.
conclusionsThe assignment of intrinsic subtypes has a significant impact in translational research for both understanding and managing breast cancer. The refined labelling, therefore, provides more accurate and reliable information by improving the source of fundamental science prior to clinical applications in medicine.
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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.