ReviewJournal of neuroscience methods2014
Sparse models for correlative and integrative analysis of imaging and genetic data.
Review in Journal of neuroscience methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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.
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Who cites it
27 citing papers in PubMed.
- Multivariate Analyses with Two-Step Dimension Reduction for an Association Study BetweenBioengineering (Basel, Switzerland) · 2025Article
- Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.The annals of applied statistics · 2024Article
- A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.American journal of human genetics · 2024Article
- Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.Briefings in bioinformatics · 2023Article
- Fused multi-modal similarity network as prior in guiding brain imaging genetic association.Frontiers in big data · 2023Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
- Recognition of Cognitive Impairment in Adult Moyamoya Disease: A Classifier Based on High-Order Resting-State Functional Connectivity Network.Frontiers in neural circuits · 2020Article
- Editorial: Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders.Frontiers in neuroscience · 2020Article
- Group sparse reduced rank regression for neuroimaging genetic study.World wide web · 2019Article
- Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.Human brain mapping · 2019Article
- Robust and Discriminative Brain Genome Association Study.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
- Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.IEEE transactions on bio-medical engineering · 2019Article
- Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals.Frontiers in oncology · 2019Article
- Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images.BMC cancer · 2018Article
- Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia.Schizophrenia bulletin · 2018Article
- Article
- FGWAS: Functional genome wide association analysis.NeuroImage · 2017Article
- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis.Machine learning in medical imaging. MLMI (Workshop) · 2017Article
- A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.
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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.