Evidence map›Paper›PMID 33737983›Full record

ArticleStatistics in biosciences2021

A super scalable algorithm for short segment detection.

Ning Hao, Yue Selena Niu, Feifei Xiao, Heping Zhang

Abstract read
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In one paragraph

Article in Statistics in biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ning HaoDepartment of Mathematics, University of Arizona, Tucson, AZ 85721.
Yue Selena NiuDepartment of Mathematics, University of Arizona, Tucson, AZ 85721.
Feifei XiaoDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29201.
Heping ZhangDepartment of Biostatistics, Yale School of Public Health, New Haven, CT 06510.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Statistical Methods in Genetic Studies of Substance UseR01DA016750 · NIDA · YALE UNIVERSITY · PI ZHANG, HEPING · 2004 to 2016
$3.3M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Analysis of Genomic and Complex DataR01HG010171 · NHGRI · YALE UNIVERSITY · PI ZHANG, HEPING · 2019 to 2022
$1.4M
NCATS NIH HHS UL1 TR001863NHGRI NIH HHS R01 HG010171NIDA NIH HHS R01 DA016750NIMH NIH HHS R01 MH116527
6 · The paper itself

Abstract

In many applications such as copy number variant (CNV) detection, the goal is to identify short segments on which the observations have different means or medians from the background. Those segments are usually short and hidden in a long sequence, and hence are very challenging to find. We study a super scalable short segment (4S) detection algorithm in this paper. This nonparametric method clusters the locations where the observations exceed a threshold for segment detection. It is computationally efficient and does not rely on Gaussian noise assumption. Moreover, we develop a framework to assign significance levels for detected segments. We demonstrate the advantages of our proposed method by theoretical, simulation, and real data studies.

Indexed as

copy number variationinferencenonparametric methodsignal detection

Identifiers

PMID33737983
PMCPMC7963345

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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.