Evidence map›Paper›PMID 38868426›Full record

ArticleiMeta2023

Ontology characterization, enrichment analysis, and similarity calculation-based evaluation of disease-syndrome-formula associations by applying SoFDA.

Yudong Liu, Jia Xu, Zecong Yu, Tong Chen, Ning Wang, Xia Du, Ping Wang, Xuezhong Zhou, Haiyu Xu, Yanqiong Zhang

Abstract read
In one paragraph

Article in iMeta, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

10 authors.

Yudong LiuInstitute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.
Jia XuGuang'anmen Hospital China Academy of Chinese Medical Sciences Beijing China.
Zecong YuInstitute of Medical Intelligence and Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University Beijing China.
Tong ChenNational Resource Center for Chinese Materia Medica Chinese Academy of Chinese Medical Sciences Beijing China.
Ning WangInstitute of Medical Intelligence and Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University Beijing China.
Xia DuInstitute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.
Ping WangInstitute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.
Xuezhong ZhouInstitute of Medical Intelligence and Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University Beijing China.
Haiyu XuInstitute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.
Yanqiong ZhangInstitute of Chinese Materia Medica China Academy of Chinese Medical Sciences Beijing China.ORCID 0000-0002-9358-111X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical symptom-based diagnosis and therapy play a crucial role in personalized medicine and drug discovery. The syndromes, distinctive groups of clinical symptoms summarized by traditional Chinese medicine (TCM) theories and clinical experiences, are used as the core diagnostic criteria and therapeutic guidance in TCM. However, there is still a lack of standardized data, information, and intrinsic molecular basis to help TCM syndromes better classify diseases and guide tailored medications. To address this problem, we built the first integrated web platform, SoFDA (http://www.tcmip.cn/Syndrome/front/), with a curated ontology of 319 TCM syndromes, 8045 diseases, and 1359 TCM herbal formulas and their relationships with genes, diseases, and formulas. This platform proposed an association measurement by calculating Jaccard/Cosine similarities between TCM syndromes and their related biomedical entities with case and control validations. On this basis, the SoFDA platform enables biomedical and pharmaceutical scientists to rank and filter the most promising associations for disease diagnosis and tailored interventions. Conversely, the targeted gene sets and symptom sets can also be associated with TCM syndromes, formulas, and diseases for function illustration. Notably, SoFDA explores the multi-way associations among diseases, TCM syndromes, symptom genes, herbal formulas, drug targets, and pathways in heterogeneous biomedical networks with lots of customization. The protocol here implements all the analyses above using the SoFDA platform. Collectively, SoFDA may provide insights into the biological basis of disease-specific TCM syndromes and the underlying molecular mechanisms, as well as a tailored treatment for single or multiple symptoms within a syndrome.

Indexed as

clinical symptom‐based diagnosis and therapydisease–syndrome–formula associationheterogeneous biomedical networkprecision medicinesimilarity calculation‐based association evaluationtraditional Chinese medicine syndrome

Identifiers

PMID38868426
PMCPMC10989962

What Socratic holds

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LicenceCC BY
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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 Socratic graph.