Evidence map›Paper›PMID 35027990›Full record

ArticleStatistical analysis and data mining2021

Scalable network estimation with

Junghi Kim, Hongtu Zhu, Xiao Wang, Kim-Anh Do

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical analysis and data mining, 2021. 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
–field-weighted citation impact
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.

  1. 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

4 authors.

Junghi KimCenter for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland.ORCID 0000-0003-2408-9436
Hongtu ZhuDepartment of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.
Xiao WangDepartment of Statistics, Purdue University, West Lafayette, Indiana.
Kim-Anh DoDepartment of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Convalescent Plasma to Limit Coronavirus Associated ComplicationsUL1TR003167 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KARP, DANIEL D, MCPHERSON, DAVID D · 2019 to 2023
$45.3M
Targeting Tumor Microenvironment-Induced Therapy Resistance in Prostate Cancer Bone MetastasisP50CA140388 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LOGOTHETIS, CHRISTOPHER J., THOMPSON, TIMOTHY CHARLES · 2009 to 2022
$23.4M
Statistical Analysis of Biomedical Imaging Data in Curved SpaceR01MH086633 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 2010 to 2018
$2.9M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Viral miRs and cellular miRs in sepsisR01GM122775 · NIGMS · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CALIN, GEORGE A., LUPU, FLOREA · 2018 to 2020
$1.1M
NCATS NIH HHS UL1 TR003167NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA140388NIGMS NIH HHS R01 GM122775NIMH NIH HHS R01 MH086633NIMH NIH HHS R01 MH116527
6 · The paper itself

Abstract

With the advent of high-throughput sequencing, an efficient computing strategy is required to deal with large genomic data sets. The challenge of estimating a large precision matrix has garnered substantial research attention for its direct application to discriminant analyses and graphical models. Most existing methods either use a lasso-type penalty that may lead to biased estimators or are computationally intensive, which prevents their applications to very large graphs. We propose using an

Indexed as

genomicsL0 penaltynetworkscalable

Identifiers

PMID35027990
PMCPMC8752083

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

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.