ArticleJournal of proteome research2018
Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.
Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 2 of them syntheses that pooled it.
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Who cites it
127 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A bibliometric and text-mining analysis of lipidomics and metabolomics in human disease.Frontiers in physiology · 2026Pooled it
- A review of omics approaches to study preeclampsia.Placenta · 2020Pooled it
- Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging.NPJ systems biology and applications · 2025Trial
- Article
- Dimensionality reduction and metabolite panel derivation in urinary metabolomics based on Random Forest with Gini index feature selection.Analytical and bioanalytical chemistry · 2026Article
- Cutting-edge advances in endocrine therapy for breast cancer (Review).Oncology letters · 2026Review
- Application of Mass Spectrometry-Based Metabolomics and Machine Learning in the Diagnostics of Lyme Neuroborreliosis.ACS omega · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Identify the PANoptosis signature and prognostic model via a multimachine-learning computational framework for bladder urothelial carcinoma.Cancer cell international · 2026Article
- Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model.Discover oncology · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- Identification and functional analysis of NADFrontiers in genetics · 2026Article
- ATF3 and HNF4A: an oxidative phosphorylation and cholesterol homeostasis-associated diagnostic and therapeutic repurposing framework target for metabolic dysfunction-associated steatohepatitis patients.Frontiers in medicine · 2026Article
- Liquid-liquid phase separation-related genes in the immune microenvironment and diagnosis of focal segmental glomerulosclerosis and minimal change disease.Renal failure · 2025Article
- Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.Translational and clinical pharmacology · 2025Article
- Identification of DNA damage response and crotonylation-related biomarkers for lung adenocarcinoma via machine learning and WGCNA.Clinical and experimental medicine · 2025Article
- Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.Scientific reports · 2025Article
- Novel anoikis-related genes for the diagnosis of non-obstructive azoospermia.Translational andrology and urology · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
67 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.
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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.