ArticlePeerJ2016
Cross-platform normalization of microarray and RNA-seq data for machine learning applications.
Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis.Urologic oncology · 2022Pooled it
- Comprehensive Bioinformatic and miRNA-Driven Analysis of the Multimeric Canonical I Kappa B Kinase (IKK) Complex in Uterine Corpus Endometrial Carcinoma (UCEC).Biomolecules · 2026Article
- Machine Learning-Derived Immune Gene Signature Predicts Prognosis and Therapeutic Vulnerabilities in Multiple Myeloma.Cancer science · 2026Article
- Article
- GANomics: bridging legacy and modern transcriptomic platforms for clinical applications.NPJ genomic medicine · 2026Article
- Evaluating Cross-Platform Batch Correction Methods for Integrated Microarray and RNA-seq Data Analysis.bioRxiv : the preprint server for biology · 2026Article
- 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
- Transplatformer: translating toxicogenomic profiles between generations of platforms.BMC bioinformatics · 2026Article
- Peroxisomal activity drives aggressive bladder cancer phenotypes and reveals erythorbic acid as a potential therapeutic modulator.Frontiers in oncology · 2026Article
- A supervised Bayesian method for time (re)annotation of transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Toward the Best Generalizable Performance of Machine Learning in Modeling Omic and Clinical Data.Laboratory investigation; a journal of technical methods and pathology · 2025Article
- A disulfidptosis-related gene signature predicts prognosis and immune-metabolic landscape in multiple myeloma.British journal of haematology · 2025Article
- Dual tissue mRNA and serum protein signatures improve risk stratification in hepatocellular carcinoma.NPJ precision oncology · 2025Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 2025Article
- Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle.International journal of molecular sciences · 2025Article
- Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy.Biomolecules · 2025Review
- Article
- Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data.Transactions on artificial intelligence · 2025Article
- Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data.BMC bioinformatics · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log 2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.
Indexed as
Identifiers
What OpenQuestion holds
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.