Evidence map›Paper›PMID 39609506›Full record

ArticleScientific reports2024

Exploration and practice of potential association prediction between diseases and drugs based on Swanson framework and bioinformatics.

Yanhua Lv, Yuyang Yuan, Xiaoyun Zhong, Qi Yu, Xuechun Lu, Baoqiang Qu, Hongxia Zhao

Abstract read
In one paragraph

Article in Scientific reports, 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

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

7 authors.

Yanhua LvShanxi Medical University, Jinzhong, China. lvyanhua01@163.com.
Yuyang YuanShanxi Medical University, Jinzhong, China.
Xiaoyun ZhongShanxi Medical University, Jinzhong, China.
Qi YuShanxi Medical University, Jinzhong, China. yuqi@sxmu.edu.cn.
Xuechun LuSecond Medical Center of the Chinese PLA General Hospital, Beijing, China.
Baoqiang QuInstitute of Scientific and Technical Information of China, Beijing, China.
Hongxia ZhaoShanxi Medical University, Jinzhong, China.

Funding

China Social Science Foundation Project 20BTQ064
6 · The paper itself

Abstract

Compared to traditional intermediate concepts, specific bioinformatics entities are more informative and higher directional. This study is based on the BITOLA system and combines bioinformatics methods to determine the intermediate concept which is key to improve efficiency of Literature-based Knowledge Discovery, proposes the concept of "Swanson framework + Bioinformatics", and conducts practice of Literature-based Knowledge Discovery to improve the scientificity and efficiency of research and development. Firstly, detected the disease related genes (i.e. differentially expressed genes) according to the results of gene functional analysis as intermediate concepts to carry out Literature-based Knowledge Discovery. Taking the disease "Autism Spectrum Disorder (ASD)" as an example, the potential "disease-drug" association was predicted, and the predicted drugs were verified from the perspective of bioinformatics. Two drugs potentially associated with ASD were found: Fish oil and Forskolin, which were closely related to ASD in bioinformatics analysis results and literature verification. The two "disease-drug" association results showed better scientificity. The BIOINF-ABC

Indexed as

Computational BiologyHumansKnowledge DiscoveryAutism Spectrum disordersBIOINF-ABC+ modelDifferentially expressed genesDrug DiscoveryLiterature-based Knowledge Discovery

Identifiers

PMID39609506
PMCPMC11604654

What Socratic 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 Socratic graph.