ArticleScientific reports2018
Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Characterization of Models for Identifying Physical and Cognitive Frailty in Older Adults With Diabetes: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Molecular Profiling of Ulcerative Colitis Subjects from the TURANDOT Trial Reveals Novel Pharmacodynamic/Efficacy Biomarkers.Journal of Crohn's & colitis · 2019Trial
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Diagnostic potential of the B9D2 gene in colorectal cancer based on whole blood gene expression data and machine learning.Discover oncology · 2025Article
- Identification and experimental validation of mitochondrial and endoplasmic reticulum stress related gene in diabetic nephropathy.Scientific reports · 2025Article
- Integrating Genetic Diversity and Agronomic Innovations for Climate-Resilient Maize Systems.Plants (Basel, Switzerland) · 2025Review
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- Bias-invariant RNA-sequencing metadata annotation.GigaScience · 2021Article
- Pheno-RNA, a method to associate genes with a specific phenotype, identifies genes linked to cellular transformation.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Maximizing the reusability of gene expression data by predicting missing metadata.PLoS computational biology · 2020Article
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
- Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.BMC bioinformatics · 2020Article
- Article
- Transcriptome-Based Prediction of Complex Traits in Maize.The Plant cell · 2020Article
- Profiling of Gene Expression Biomarkers as a Classifier of Methotrexate Nonresponse in Patients With Rheumatoid Arthritis.Arthritis & rheumatology (Hoboken, N.J.) · 2019Article
- Understanding the hidden relations between pro- and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method.Scientific reports · 2019Article
- Integrating Gene Expression Data Into Genomic Prediction.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.
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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.