Evidence map›Paper›PMID 38709887›Full record

ArticleNucleic acids research2024

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

Chia-Hsin Liu, Pei-Chun Shen, Wen-Jen Lin, Hsiu-Cheng Liu, Meng-Hsin Tsai, Tzu-Ya Huang, I-Chieh Chen, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung and 1 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  11. LipidFun: a database of lipid functions.Bioinformatics (Oxford, England) · 2025
    Article
  12. Review
  13. Review
  14. Article
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  16. 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

11 authors.

Chia-Hsin LiuCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Pei-Chun ShenCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Wen-Jen LinCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Hsiu-Cheng LiuCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Meng-Hsin TsaiCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Tzu-Ya HuangCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
I-Chieh ChenCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Yo-Liang LaiDepartment of Radiation Oncology, China Medical University, Taichung 404328, Taiwan.
Yu-De WangGraduate Institute of Biomedical Sciences, China Medical University, Taichung 404328, Taiwan.
Mien-Chie HungCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.
Wei-Chung ChengCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 404328, Taiwan.ORCID 0000-0002-1229-4857

Funding

China Medical University CMU112-MF-25China Medical University Hospital DMR-112-056National Science and Technology Council NSTC 112-2311-B-039 -001
6 · The paper itself

Abstract

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

Indexed as

LipidomicsLipidsSoftwareAlgorithmsData AnalysisData VisualizationInternetLipids

Identifiers

PMID38709887
PMCPMC11223864

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.