ArticleJournal of translational medicine2009
A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.
Article in Journal of translational medicine, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 66 citations in OpenAlex.
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- 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
- Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 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
- Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking.Sensors (Basel, Switzerland) · 2020Article
- Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework.Pharmaceuticals (Basel, Switzerland) · 2020Article
- Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: MolecularMolecules (Basel, Switzerland) · 2020Review
- 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
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
- 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
- Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.Scientific reports · 2017Article
- Machine learning and systems genomics approaches for multi-omics data.Biomarker research · 2017Review
- Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis.Neurology (E-Cronicon) · 2016Article
- Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome.Human genomics · 2015Article
- Application of high-dimensional feature selection: evaluation for genomic prediction in man.Scientific reports · 2015Article
- Myalgic Encephalomyelitis: Symptoms and Biomarkers.Current neuropharmacology · 2015Review
- A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP.PloS one · 2014Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).
methodsWe employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.
resultsThe naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.
conclusionWe demonstrated that our approach is a promising method to assess the associations between CFS and SNPs.
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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.