Dataset Information

Integrative multi-view regression: Bridging group-sparse and low-rank models.

ABSTRACT: Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. We cast this new problem under the familiar multivariate regression framework and propose an integrative reduced-rank regression (iRRR), where each view has its own low-rank coefficient matrix. As such, latent features are extracted from each view in a supervised fashion. For model estimation, we develop a convex composite nuclear norm penalization approach, which admits an efficient algorithm via alternating direction method of multipliers. Extensions to non-Gaussian and incomplete data are discussed. Theoretically, we derive non-asymptotic oracle bounds of iRRR under a restricted eigenvalue condition. Our results recover oracle bounds of several special cases of iRRR including Lasso, group Lasso, and nuclear norm penalized regression. Therefore, iRRR seamlessly bridges group-sparse and low-rank methods and can achieve substantially faster convergence rate under realistic settings of multi-view learning. Simulation studies and an application in the Longitudinal Studies of Aging further showcase the efficacy of the proposed methods.

SUBMITTER: Li G

PROVIDER: S-EPMC6849205 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Integrative multi-view regression: Bridging group-sparse and low-rank models.

Li Gen G Liu Xiaokang X Chen Kun K

Biometrics 20190329 2

Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, and the predictors within each relevant view contribute to the prediction collectively rather than sparsely. We cast this new problem under the familiar multivariate regression frame ...[more]

PMID: 30456759

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Project description:Summary Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors. Highlights • A multi-domain integrative Swin transformer was proposed for spare-data imaging• A trainable image edge enhancement filter recovers sharp image edges and features• A modified vision transformer first demonstrates its great potential in tomography• Patient clinical cardiac and numerical data demonstrate our MIST power The bigger picture Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve reconstructed image quality from sparse-view data, we develop a multi-domain integrative Swin transformer (MIST) network in this study. The proposed MIST-net incorporates lavish domain features from data, residual data, image, and residual image using flexible network architectures, which help deeply mine the data and image features. To detect image features and protect image edges, the trainable edge enhancement filter is further incorporated to the network for improving encode-decode ability. A high-quality reconstruction transformer was designed to improve the ability of global feature extraction. Our results from both simulation and real cardiac data demonstrated the great potential of MIST. A multi-domain integrative Swin transformer network (MIST-net) demonstrates feasibility, advantages, and great potential in sparse-view tomographic reconstruction. MIST-net mainly integrates data, image, residual data, and residual image domain information into a unified model with a trainable edge enhancement filter and advanced transformer AI technique to realize ultra-sparse-data (48 views) high-fidelity imaging.

Project description:A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the "small sample, but large variables" problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other studies.

Dataset Information

Integrative multi-view regression: Bridging group-sparse and low-rank models.

Publications

Integrative multi-view regression: Bridging group-sparse and low-rank models.

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