Evidence map›Paper›PMID 31501637›Full record

ArticleCommunications in statistics: Simulation and computation2017

FDR control of detected regions by multiscale matched filtering.

Nezamoddin N Kachouie, Xihong Lin, Armin Schwartzman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications in statistics: Simulation and computation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach.Communications in statistics. Case studies, data analysis and applications · 2015
    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

3 authors.

Nezamoddin N KachouieDepartment of Mathematical Sciences, Florida Institute of Technology, Melbourne, FL, USA.
Xihong LinDepartment of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
Armin SchwartzmanDepartment of Statistics, North Carolina State University, Raleigh, NC, USA.

Funding

Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Multiple testing methods for random fields and high-dimensional dependent dataR01CA157528 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SCHWARTZMAN, ARMIN · 2012 to 2017
$1.3M
NCI NIH HHS P01 CA134294NCI NIH HHS R01 CA157528NCI NIH HHS R35 CA197449
6 · The paper itself

Abstract

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multi-scale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal to noise ratio (SNR) via matched filtering. This is achieved by considering a one-dimensional (1D) region detection problem as its equivalent 0D (zero dimensional) peak detection problem. The detection method does not require

Indexed as

False discovery rateFeature extractionKernel regressionLocal polynomial regressionMatched filteringMultiple testingRegion detectionSignal reconstruction

Identifiers

PMID31501637
PMCPMC6733272

What OpenQuestion holds

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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.