ArticleCancers2021
Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
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Who cites it
20 citing papers in PubMed, 39 citations in OpenAlex.
- A cfDNA-based DNA methylation classifier for distinguishing prostate cancer from benign prostatic hyperplasia.Prostate international · 2026Article
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Νovel methylation biomarkers in liquid biopsy and classifying biosignatures for the clinical management of breast cancer.Breast cancer research : BCR · 2026Article
- BRCA1 & BRCA2 methylation as a prognostic and predictive biomarker in cancer: Implementation in liquid biopsy in the era of precision medicine.Clinical epigenetics · 2024Review
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.IBRO neuroscience reports · 2023Article
- Methylation Profile of Small Breast Cancer Tumors Evaluated by Modified MS-HRM.International journal of molecular sciences · 2023Article
- Pharmacogenomic-guided dosing of fluoropyrimidines beyondFrontiers in pharmacology · 2023Review
- A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity.Scientific reports · 2022Article
- Prediction and Ranking of Biomarkers UsingInternational journal of molecular sciences · 2022Article
- Just Add Data: automated predictive modeling for knowledge discovery and feature selection.NPJ precision oncology · 2022Article
- Article
- Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.International journal of molecular sciences · 2022Article
- Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning.Journal of clinical medicine · 2022Article
- PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.Frontiers in molecular biosciences · 2022Article
- Emerging Roles of Long Noncoding RNAs in Breast Cancer Epigenetics and Epitranscriptomics.Frontiers in cell and developmental biology · 2022Review
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Relapse in Prostate Cancer.Frontiers in oncology · 2022Article
- Article
- Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.Scientific reports · 2021Article
- Methylation Status of Corticotropin-Releasing Factor (CRF) Receptor Genes in Colorectal Cancer.Journal of clinical medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.
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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.