Evidence map›Paper›PMID 33746241›Full record

ArticleIEEE transactions on information theory2019

SOFAR: Large-Scale Association Network Learning.

Yoshimasa Uematsu, Yingying Fan, Kun Chen, Jinchi Lv, Wei Lin

Open access · greenAbstract read

In one paragraph

Article in IEEE transactions on information theory, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

3.5field-weighted citation impact, top 6% of its field

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 43 citations in OpenAlex.

DeepLINK: Deep learning inference using knockoffs with applications to genomics.Proceedings of the National Academy of Sciences of the United States of America · 2021
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Yoshimasa UematsuYoshimasa Uematsu is Assistant Professor, Department of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Yingying Fan is Dean's Associate Professor in Business Administration, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Kun Chen is Associate Professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Wei Lin is Assistant Professor, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China 100871.

Yingying FanYoshimasa Uematsu is Assistant Professor, Department of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Yingying Fan is Dean's Associate Professor in Business Administration, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Kun Chen is Associate Professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Wei Lin is Assistant Professor, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China 100871.

Kun ChenYoshimasa Uematsu is Assistant Professor, Department of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Yingying Fan is Dean's Associate Professor in Business Administration, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Kun Chen is Associate Professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Wei Lin is Assistant Professor, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China 100871.

Jinchi LvYoshimasa Uematsu is Assistant Professor, Department of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Yingying Fan is Dean's Associate Professor in Business Administration, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Kun Chen is Associate Professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Wei Lin is Assistant Professor, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China 100871.

Wei LinYoshimasa Uematsu is Assistant Professor, Department of Economics and Management, Tohoku University, Sendai 980-8576, Japan. Yingying Fan is Dean's Associate Professor in Business Administration, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Kun Chen is Associate Professor, Department of Statistics, University of Connecticut, Storrs, CT 06269. Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor, Data Sciences and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, CA 90089. Wei Lin is Assistant Professor, School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China 100871.

Tohoku University · JPUniversity of Southern California · USUniversity of Connecticut · US

Funding

An integrative statistics-guided image-based multi-scale lung modelU01HL114494 · NHLBI · UNIVERSITY OF IOWA · PI LIN, CHING-LONG · 2013 to 2017

$3.1M

Adaptive Reproducible High-Dimensional Nonlinear Inference for Big Biological DataR01GM131407 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FAN, YINGYING · 2018 to 2021

$1.1M

NHLBI NIH HHS U01 HL114494NIGMS NIH HHS R01 GM131407

6 · The paper itself

Abstract

Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this paper we suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications to both unsupervised and supervised learning tasks such as biclustering with sparse singular value decomposition, sparse principal component analysis, sparse factor analysis, and spare vector autoregression analysis. Exploiting the framework of convexity-assisted nonconvex optimization, we derive nonasymptotic error bounds for the suggested procedure characterizing the theoretical advantages. The statistical guarantees are powered by an efficient SOFAR algorithm with convergence property. Both computational and theoretical advantages of our procedure are demonstrated with several simulations and real data examples.

Indexed as

Big dataLarge-scale association networkLatent factorsNonconvex statistical learningOrthogonality constrained optimizationSimultaneous response and predictor selectionSparse singular value decomposition

Identifiers

PMID33746241

DOI10.1109/tit.2019.2909889

PMCPMC7970712

OpenAlexW2963233138

What OpenQuestion holds

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Registered trials

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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.