Evidence map›Paper›PMID 35045824›Full record

ArticleBMC bioinformatics2022

Detect the early-warning signals of diseases based on signaling pathway perturbations on a single sample.

Yanhao Huo, Geng Zhao, Luoshan Ruan, Peng Xu, Gang Fang, Fengyue Zhang, Zhenshen Bao, Xin Li

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 45% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

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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

8 authors at 5 institutions in 1 country.

Yanhao HuoInstitute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
Geng ZhaoNetease Youdao Information Technology (Hangzhou) Co., Ltd., Hangzhou, 310000, Zhejiang, China.
Luoshan RuanDepartment of Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430000, Hubei, China.
Peng XuInstitute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
Gang FangInstitute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China.
Fengyue ZhangDepartment of Biomedical Engineering, School of Life Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
Zhenshen BaoInstitute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, Guangdong, China. bzsbao@163.com.ORCID http://orcid.org/0000-0002-6376-7124
Xin LiDepartment of Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430000, Hubei, China. xin.li@whu.edu.cn.
Guangzhou University · CNWuhan University · CNBeijing Institute of Technology · CNGuangdong University of Technology · CNNetEase (China) · CN

Funding

National Natural Science Foundation of China 62002079National Natural Science Foundation of China 62072128
6 · The paper itself

Abstract

backgroundDuring the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect.

resultsAbnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt.

conclusionsThese results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

Indexed as

Phosphatidylinositol 3-KinasesSignal TransductionEntropyPhosphatidylinositol 3-KinasesEarly warning signalsPre-disease stateSignaling pathwaySignal perturbationSingle sample

Identifiers

PMID35045824
PMCPMC8772045
OpenAlexW4205605449

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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.