ArticleProceedings. IEEE International Symposium on Biomedical Imaging2019
DIAGNOSIS STATUS GUIDED BRAIN IMAGING GENETICS VIA INTEGRATED REGRESSION AND SPARSE CANONICAL CORRELATION ANALYSIS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Exploring Brain Structural and Functional Biomarkers in SchizophreniaFrontiers in neuroscience · 2022Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Brain imaging genetics use the imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in imaging genetics. The regression only selects relevant features for predictors. SCCA overcomes this but is unsupervised and thus could not make use of the diagnosis information. We propose a novel method integrating regression and SCCA together to construct a supervised sparse bi-multivariate learning model. The regression part plays a role of providing guidance for imaging QTs selection, and the SCCA part is focused on selecting relevant genetic markers and imaging QTs. We propose an efficient algorithm based on the alternative search method. Our method obtains better feature selection results than both regression and SCCA on both synthetic and real neuroimaging data. This demonstrates that our method is a promising bi-multivariate tool for brain imaging genetics.
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What Socratic 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 Socratic graph.