ArticleBMC medical genomics2013
Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method.
Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank.Schizophrenia research · 2022Article
- Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.IEEE journal of biomedical and health informatics · 2020Article
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NeuroImage · 2017Review
- Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance.Schizophrenia bulletin · 2016Article
- Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19.BMC genetics · 2016Article
- Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure.BMC proceedings · 2016Article
- Sparse models for correlative and integrative analysis of imaging and genetic data.Journal of neuroscience methods · 2014Review
- Joint Sparse Collaborative Regression on Imaging Genetics Study of Schizophrenia.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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5 authors.
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Abstract
backgroundIn recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis.
methodsIn this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method.
resultsResults showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data.
conclusionsThe proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.
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