Evidence map›Paper›PMID 40080702›Full record

ArticleBioinformatics (Oxford, England)2025

LipidFun: a database of lipid functions.

Wen-Jen Lin, Chia-Hsin Liu, Ming-Siang Huang, Pei-Chun Shen, Hsiu-Cheng Liu, Meng-Hsin Tsai, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung, Nai-Wen Chang and 1 more

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. 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

11 authors.

Wen-Jen LinSchool of Medicine, China Medical University, Taichung 404333, Taiwan.
Chia-Hsin LiuCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.ORCID 0000-0002-2880-8365
Ming-Siang HuangDepartment of Computer Science & Information Engineering, Asia University, Taichung 41354, Taiwan.
Pei-Chun ShenCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.
Hsiu-Cheng LiuCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.
Meng-Hsin TsaiCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.
Yo-Liang LaiDepartment of Radiation Oncology, China Medical University, Taichung 404328, Taiwan.
Yu-De WangGraduate Institute of Biomedical Science, China Medical University, Taichung 404333, Taiwan.
Mien-Chie HungCancer Biology and Precision Therapeutics Center, China Medical University, Taichung 406040, Taiwan.
Nai-Wen ChangDepartment of Medical Research, National Taiwan University Hospital, Taipei 100229, Taiwan.
Wei-Chung ChengGraduate Institute of Biomedical Science, China Medical University, Taichung 404333, Taiwan.ORCID 0000-0002-1229-4857

Funding

Cancer Biology and Precision Therapeutics Center, China Medical UniversityChina Medical University Hospital, TaiwanChina Medical University, TaiwanNational Science and Technology Council, Taiwan 113-2311-B-039-001-MY3
6 · The paper itself

Abstract

motivationLipids play crucial roles in various biological functions and diseases. However, a gap exists in databases providing information of lipids functions based on curated information. Consequently, LipidFun is purposed as the first lipid function database with sentence-level evidence detailing lipid-related phenotypes and biological functions.

resultsPotential lipid functions were extracted from the biomedical literature using natural language processing techniques, with accuracy and reliability ensured through manual curation by four domain experts. LipidFun constructs classification systems for lipids, biological functions, and phenotypes for named entity recognition. Sentence-level evidence is extracted to highlight connections to lipid-associated biological processes and diseases. Integrating these classification systems and a large amount of sentence-level evidence allows LipidFun to provide an overview of lipid-phenotype and lipid-biological function associations through concise visualizations. Overall, LipidFun unravels the relationships between lipids and biological mechanisms, underscoring their overarching influence on physiological processes. AVAILABILITY AND IMPLEMENTATION: LipidFun is available at https://lipidfun.bioinfomics.org/.

Indexed as

Databases, FactualLipid MetabolismLipidsComputational BiologyHumansNatural Language ProcessingPhenotypeSoftwareLipids

Identifiers

PMID40080702
PMCPMC11974499

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.