Evidence map›Paper›PMID 38045372›Full record

ArticlebioRxiv : the preprint server for biology2024

SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization.

Weixuan Liu, Thao Vu, Iain Konigsberg, Katherine Pratte, Yonghua Zhuang, Katerina Kechris

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

6 authors.

Weixuan LiuDepartment of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.ORCID 0009-0005-0222-1784
Thao VuDepartment of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.
Iain KonigsbergDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.ORCID 0000-0001-7356-100X
Katherine PratteDepartment of Biostatistics, National Jewish Health, Denver, 80206, CO, USA.
Yonghua ZhuangDepartment of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.
Katerina KechrisDepartment of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, 80045, CO, USA.

Funding

Multi-omic networks associated with COPD progression in TOPMed CohortsR01HL152735 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI BANAEI-KASHANI, FARNOUSH, BOWLER, RUSSELL PAUL · 2020 to 2023
$3.1M
NHLBI NIH HHS R01 HL152735
6 · The paper itself

Abstract

Summary: Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. Availability: This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

Indexed as

automated pipelinemulti-omics integrationnetwork analysis

Identifiers

PMID38045372
PMCPMC10690212

What OpenQuestion holds

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LicenceCC BY
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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.