Evidence map›Paper›PMID 28137712›Full record

ArticleBioinformatics (Oxford, England)2017

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

Sumanta Basu, William Duren, Charles R Evans, Charles F Burant, George Michailidis, Alla Karnovsky

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.

0numbers the graph read from it
0cells of the map it votes in
131citing 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

131 citing papers in PubMed.

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  11. CorrelandJournal of the American Society for Mass Spectrometry · 2026
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71 more citing papers are in PubMed but not listed here.

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

6 authors.

Sumanta BasuDepartment of Statistics, University of California, Berkeley, CA, USA.
William DurenDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.
Charles R EvansDepartment of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
Charles F BurantDepartment of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
George MichailidisDepartment of Statistics, University of Florida, Gainesville, FL, USA.
Alla KarnovskyDepartment of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.

Funding

WOMENS HEALTH ACROSS THE NATION--COORDINATING CENTERU01AG012553 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BROOKS, MARIA MORI · 1994 to 2019
$37.7M
SWAN Repository IVU01AG017719 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HARLOW, SIOBAN D · 2000 to 2020
$25.0M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Joyce Lee · 2010 to 2026
$20.3M
WOMENS HEALTH ACROSS THE NATION--ENDOCRINE LABU01AG012495 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MCCONNELL, DANIEL S · 1994 to 2018
$18.8M
WOMENS HEALTH ACROSS THE NATION--PITTSBURGHU01AG012546 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MATTHEWS, KAREN · 1994 to 2019
$16.2M
WOMENS HEALTH ACROSS THE NATION--UCDAVISU01AG012554 · NIA · UNIVERSITY OF CALIFORNIA DAVIS · PI GOLD, ELLEN B. · 1994 to 2019
$14.5M
WOMENS HEALTH ACROSS THE NATION--UCLAU01AG012539 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GREENDALE, GAIL A · 1994 to 2018
$13.7M
THE STUDY OF WOMEN'S HEALTH ACROSS THE NATION-MICHIGANU01NR004061 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HARLOW, SIOBAN D · 1994 to 2018
$12.9M
WOMENS HEALTH ACROSS THE NATION--CHICAGOU01AG012505 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI KRAVITZ, HOWARD M · 1994 to 2019
$11.9M
WOMENS HEALTH ACROSS THE NATION--MASSACHUSETTS GENERAL HU01AG012531 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FINKELSTEIN, JOEL S · 1994 to 2019
$10.7M
Women's Health Across the Nation-New Jersey SiteU01AG012535 · NIA · UNIV OF MED/DENT OF NJ-NJ MEDICAL SCHOOL · PI DERBY, CAROL A. · 1994 to 2019
$9.2M
Machine Learning Tools for Discovery and Analysis of Active Metabolic PathwaysR01GM114029 · NIGMS · UNIVERSITY OF WASHINGTON · PI SHOJAIE, ALI · 2016 to 2020
$1.7M
6 · The paper itself

Abstract

motivationRecent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.

resultsLeveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATION: http://metscape.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsMetabolic Networks and PathwaysModels, BiologicalAdultFemaleHumansMass SpectrometryMetabolomicsMiddle Aged

Identifiers

PMID28137712
PMCPMC5860222

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.