ArticleCommunications biology2023
Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A Three-Gene Prognostic Signature Driven by an ER Stress-Associated ceRNA Network: Integrating Single-Cell Transcriptomics and Cross-Platform Validation in Hepatocellular Carcinoma.Current issues in molecular biology · 2026Article
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- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases.Journal of clinical and translational pathology · 2026Article
- Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.Frontiers in bioinformatics · 2026Article
- CancerSubtypeXplore: A Modular Platform for Multiomics Cancer Subtype Prediction and Biomarker Consensus Discovery.Computational and structural biotechnology journal · 2026Article
- Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- Pulmonary Fibrosis-Focusing on the Future: Aspen Lung Conference 2024 Summary.American journal of respiratory cell and molecular biology · 2025Article
- Developing a thyroid cancer differentiation state classification system using deep residual networks and metabolic signature profiling.NPJ digital medicine · 2025Article
- Redefining Breast Cancer Care by Harnessing Computational Drug Repositioning.Medicina (Kaunas, Lithuania) · 2025Review
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
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- Transcriptomic analysis of human cartilage identified potential therapeutic targets for hip osteoarthritis.Human molecular genetics · 2025Article
- Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM.Cancers · 2025Article
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- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.PloS one · 2025Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Investigating heart rate variability measures during pregnancy as predictors of postpartum depression and anxiety: an exploratory study.Translational psychiatry · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA assays were run on microarray, while RNA-seq is now the platform of choice for many new experiments. The data structure and distributions between the platforms differ, making it challenging to combine them directly. Here we perform supervised and unsupervised machine learning evaluations to assess which existing normalization methods are best suited for combining microarray and RNA-seq data. We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously. Nonparanormal normalization and z-scores are also appropriate for some applications, including pathway analysis with Pathway-Level Information Extractor (PLIER). We demonstrate that it is possible to perform effective cross-platform normalization using existing methods to combine microarray and RNA-seq data for machine learning applications.
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Registered trials
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