ReviewNeuroImage2014
Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.
Review in NeuroImage, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
26 citing papers in PubMed.
- Diagnostic value of multiparametric MRI combined with an interpretable machine learning model in the differentiation of benign and malignant ovarian-adnexal lesions classified as O-RADS 4.BMC medical imaging · 2025Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Article
- Groupwise structural sparsity for discriminative voxels identification.Frontiers in neuroscience · 2023Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions.Brain sciences · 2020Article
- Recognition of Cognitive Impairment in Adult Moyamoya Disease: A Classifier Based on High-Order Resting-State Functional Connectivity Network.Frontiers in neural circuits · 2020Article
- A core collection of pan-schizophrenia genes allows building cohort-specific signatures of affected brain.Scientific reports · 2019Article
- Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals.Frontiers in oncology · 2019Article
- Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review.Neuropsychiatric disease and treatment · 2019Review
- Enforcing Co-Expression Within a Brain-Imaging Genomics Regression Framework.IEEE transactions on medical imaging · 2018Article
- Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images.BMC cancer · 2018Article
- A curated target gene pool assisting disease prediction and patient-specific biomarker selection for lung squamous cell carcinoma.Oncology letters · 2018Article
- Knowledge database assisted gene marker selection for chronic lymphocytic leukemia.The Journal of international medical research · 2018Article
- A Curated Target Gene Pool Assisting Early Disease Prediction and Patient-Specific Treatment for Small Cell Lung Cancer.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI.Biomedical engineering online · 2018Article
- Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges.International journal of molecular sciences · 2018Review
- Article
- Depression in patients with SAPHO syndrome and its relationship with brain activity and connectivity.Orphanet journal of rare diseases · 2017Article
- A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI.Journal of neuroscience methods · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for uni-variant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis.
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What OpenQuestion holds
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.