ArticleScientific reports2019
Elastic net-based prediction of IFN-β treatment response of patients with multiple sclerosis using time series microarray gene expression profiles.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Pharmacoepigenetic Biomarkers in Inflammatory Bowel Diseases: A Narrative Review.The Yale journal of biology and medicine · 2025Review
- Two-Step Transfer Learning Improves Deep Learning-Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma.Bioinformatics and biology insights · 2025Article
- A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR.Frontiers in bioengineering and biotechnology · 2022Article
- Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria.Diagnostics (Basel, Switzerland) · 2021Article
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- A recursive framework for predicting the time-course of drug sensitivity.Scientific reports · 2020Article
- Type I Interferon (IFN)-Regulated Activation of Canonical and Non-Canonical Signaling Pathways.Frontiers in immunology · 2020Review
- Identification of Monotonically Differentially Expressed Genes for IFN-BioMed research international · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INF-β has been widely used to treat patients with multiple sclerosis (MS) in relapse. Accurate prediction of treatment response is important for effective personalization of treatment. Microarray data have been frequently used to discover new genes and to predict treatment responses. However, conventional analytical methods suffer from three difficulties: high-dimensionality of datasets; high degree of multi-collinearity; and achieving gene identification in time-course data. The use of Elastic net, a sparse modelling method, would decrease the first two issues; however, Elastic net is currently unable to solve these three issues simultaneously. Here, we improved Elastic net to accommodate time-course data analyses. Numerical experiments were conducted using two time-course microarray datasets derived from peripheral blood mononuclear cells collected from patients with MS. The proposed methods successfully identified genes showing a high predictive ability for INF-β treatment response. Bootstrap sampling resulted in an 81% and 78% accuracy for each dataset, which was significantly higher than the 71% and 73% accuracy obtained using conventional methods. Our methods selected genes showing consistent differentiation throughout all time-courses. These genes are expected to provide new predictive biomarkers that can influence INF-β treatment for MS patients.
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