ArticleIEEE transactions on big data
Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers.
Article in IEEE transactions on big data. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach.IEEE journal of translational engineering in health and medicine · 2024Article
- Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- LARGE-SCALE MULTIVARIATE SPARSE REGRESSION WITH APPLICATIONS TO UK BIOBANK.The annals of applied statistics · 2022Article
- Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer's Disease Diagnosis.Frontiers in aging neuroscience · 2022Article
- Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.Current genomics · 2021Review
- Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification.Medical & biological engineering & computing · 2021Article
- DEEP NETWORK-BASED FEATURE SELECTION FOR IMAGING GENETICS: APPLICATION TO IDENTIFYING BIOMARKERS FOR PARKINSON'S DISEASE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Group sparse reduced rank regression for neuroimaging genetic study.World wide web · 2019Article
- A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis.Neuroinformatics · 2018Article
- Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2018Article
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Authors and funding
4 authors.
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Abstract
In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-Wide association study. Specifically, we impose a low-rank constraint on the weight coefficient matrix and then decompose it into two low-rank matrices, which find relationships in genetic features and in brain imaging features, respectively. We also introduce a sparse acyclic digraph with sparsity-inducing penalty to take further into account the correlations among the genetic variables, by which it can be possible to identify the representative SNPs that are highly associated with the brain imaging features. We optimize our objective function by jointly tackling low-rank regression and variable selection in a framework. In our method, the low-rank constraint allows us to conduct variable selection with the low-rank representations of the data; the learned low-sparsity weight coefficients allow discarding unimportant variables at the end. The experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that the proposed method could select the important SNPs to more accurately estimate the brain imaging features than the state-of-the-art methods.
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