ArticleJournal of the American Statistical Association2016
Structured Matrix Completion with Applications to Genomic Data Integration.
Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Representation Retrieval Learning for Heterogeneous Data Integration.Journal of the American Statistical Association · 2026Article
- Empirical Bayes Linked Matrix Decomposition.Machine learning · 2024Article
- CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.Computers in biology and medicine · 2024Article
- Graphical Model Inference with Erosely Measured Data.Journal of the American Statistical Association · 2024Article
- Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data.Psychometrika · 2023Article
- Using interpretable machine learning to extend heterogeneous antibody-virus datasets.Cell reports methods · 2023Article
- CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data.Research square · 2023Article
- Article
- PENALIZED REGRESSION FOR MULTIPLE TYPES OF MANY FEATURES WITH MISSING DATA.Statistica Sinica · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Sample-wise Combined Missing Effect Model with Penalization.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Generalized Liquid Association Analysis for Multimodal Data Integration.Journal of the American Statistical Association · 2023Article
- Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods.Environmental health perspectives · 2022Article
- High-dimensional integrative copula discriminant analysis for multiomics data.Statistics in medicine · 2020Article
- A general framework for integrative analysis of incomplete multiomics data.Genetic epidemiology · 2020Article
- Generalized integrative principal component analysis for multi-type data with block-wise missing structure.Biostatistics (Oxford, England) · 2020Article
- Simultaneous Covariance Inference for Multimodal Integrative Analysis.Journal of the American Statistical Association · 2020Article
- Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation.Journal of the American Statistical Association · 2020Article
- Article
- Structured Matrix Completion with Applications to Genomic Data Integration.Journal of the American Statistical Association · 2016Article
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
Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.
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.