ArticleMolecular diagnosis & therapy2008
A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy.
Article in Molecular diagnosis & therapy, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed, 30 citations in OpenAlex.
- Pattern Recognition Algorithms in Pharmacogenomics and Drug Repurposing-Case Study: Ribavirin and Lopinavir.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Review
- Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach.Journal of personalized medicine · 2021Article
- Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.Scientific reports · 2021Article
- Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions.Scientific reports · 2021Article
- Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework.Pharmaceuticals (Basel, Switzerland) · 2020Article
- An Ensemble Approach to Predict Schizophrenia Using Protein Data in the N-methyl-D-Aspartate Receptor (NMDAR) and Tryptophan Catabolic Pathways.Frontiers in bioengineering and biotechnology · 2020Article
- Combination ofFrontiers in psychiatry · 2018Article
- A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers.Frontiers in psychiatry · 2018Article
- Machine learning and systems genomics approaches for multi-omics data.Biomarker research · 2017Review
- A critical assessment of feature selection methods for biomarker discovery in clinical proteomics.Molecular & cellular proteomics : MCP · 2013Article
- Assessing gene-gene interactions in pharmacogenomics.Molecular diagnosis & therapy · 2012Review
- Pharmacogenomics of chronic hepatitis C therapy with genome-wide association studies.Journal of experimental pharmacology · 2010Review
- Pilot study of an association between a common variant in the non-muscle myosin heavy chain 9 (MYH9) gene and type 2 diabetic nephropathy in a Taiwanese population.The application of clinical genetics · 2010Article
- Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms.Advances and applications in bioinformatics and chemistry : AABC · 2010Article
- A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.Journal of translational medicine · 2009Article
- Review
- Association study of a brain-derived neurotrophic-factor polymorphism and short-term antidepressant response in major depressive disorders.Pharmacogenomics and personalized medicine · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterferon-alpha (IFNalpha) in combination with ribavirin can be used for the treatment of patients with chronic hepatitis C. This therapeutic approach achieves an overall sustained response rate of approximately 40%, but treatment takes 6-12 months and patients often experience significant adverse reactions.
objectiveWe aim to develop a tool to distinguish potential responders from nonresponders prior to initiation of IFNalpha-ribavirin treatment.
methodsUsing single nucleotide polymorphisms (SNPs) and viral genotype, we applied the support vector machine (SVM) algorithm to build a tool to predict responsiveness to IFNalpha-ribavirin combination therapy. Furthermore, we utilized the SVM algorithm with the recursive feature elimination method to identify a subset of factors that are significantly more influential than the others. RESULTS AND
conclusionThe SVM model is a promising method for inferring responsiveness to IFNalpha dealing with the complex nonlinear relationship between factors (such as SNPs and viral genotype) and successful therapy. In this study, we demonstrate that our tool may allow patients and doctors to make more informed decisions by analyzing host SNP and viral genotype information.
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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.