ArticleBiometrics2022
Sparse linear discriminant analysis for multiview structured data.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.Data science in science · 2026Article
- Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.Biostatistics (Oxford, England) · 2025Article
- CellMentor: cell-type aware dimensionality reduction for single-cell RNA-sequencing data.Nature communications · 2025Article
- Unravelling the impacts of captivity on saltwater crocodile (Crocodylus porosus) cloacal bacterial communities and physiology.FEMS microbiology ecology · 2025Article
- Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.Statistics in medicine · 2025Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
- Proteomic and genetic predictors and risk scores of cardiovascular diseases in persons living with HIV.Frontiers in cardiovascular medicine · 2025Article
- HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.Briefings in bioinformatics · 2024Article
- Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data.Computational statistics & data analysis · 2024Article
- Molecular Evaluation of the mRNA Expression of theJournal of fungi (Basel, Switzerland) · 2024Article
- DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.Briefings in bioinformatics · 2024Article
- Comparative analysis of integrative classification methods for multi-omics data.Briefings in bioinformatics · 2024Article
- Interpretable deep learning methods for multiview learning.BMC bioinformatics · 2024Article
- mvlearnR and Shiny App for multiview learning.Bioinformatics advances · 2024Article
- Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers.Bioinformatics and biology insights · 2024Article
- Deep IDA: a deep learning approach for integrative discriminant analysis of multi-omics data with feature ranking-an application to COVID-19.Bioinformatics advances · 2024Article
- Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status.BMC genomics · 2023Article
- Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease.Biostatistics (Oxford, England) · 2022Article
- sJIVE: Supervised Joint and Individual Variation Explained.Computational statistics & data analysis · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two-step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.
Indexed as
Identifiers
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.