Evidence map›Paper›PMID 37198530›Full record

ArticleBMC bioinformatics2023

Identification of essential proteins based on edge features and the fusion of multiple-source biological information.

Peiqiang Liu, Chang Liu, Yanyan Mao, Junhong Guo, Fanshu Liu, Wangmin Cai, Feng Zhao

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

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.8field-weighted citation impact, top 15% 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

1 citing paper in PubMed, 12 citations in OpenAlex.

  1. 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

7 authors at 1 institution in 1 country.

Peiqiang LiuSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China. liupq@126.com.
Chang LiuSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Yanyan MaoSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Junhong GuoSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Fanshu LiuSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Wangmin CaiSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Feng ZhaoSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Shandong Institute of Business and Technology · CN

Funding

Education Quality Improvement Plan for Graduate Students of Shandong Province, China SDYKC19199National Natural Science Foundation of China 62176140Natural Science Foundation of Shandong Province ZR2022MA076
6 · The paper itself

Abstract

backgroundA major current focus in the analysis of protein-protein interaction (PPI) data is how to identify essential proteins. As massive PPI data are available, this warrants the design of efficient computing methods for identifying essential proteins. Previous studies have achieved considerable performance. However, as a consequence of the features of high noise and structural complexity in PPIs, it is still a challenge to further upgrade the performance of the identification methods.

methodsThis paper proposes an identification method, named CTF, which identifies essential proteins based on edge features including h-quasi-cliques and uv-triangle graphs and the fusion of multiple-source information. We first design an edge-weight function, named EWCT, for computing the topological scores of proteins based on quasi-cliques and triangle graphs. Then, we generate an edge-weighted PPI network using EWCT and dynamic PPI data. Finally, we compute the essentiality of proteins by the fusion of topological scores and three scores of biological information.

resultsWe evaluated the performance of the CTF method by comparison with 16 other methods, such as MON, PeC, TEGS, and LBCC, the experiment results on three datasets of Saccharomyces cerevisiae show that CTF outperforms the state-of-the-art methods. Moreover, our method indicates that the fusion of other biological information is beneficial to improve the accuracy of identification.

Indexed as

Saccharomyces cerevisiae ProteinsAlgorithmsComputational BiologyProtein Interaction MappingProtein Interaction MapsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsDynamic protein–protein interaction networkEssential proteinFusion methodQuasi-cliqueTriangle graph

Identifiers

PMID37198530
PMCPMC10193741
OpenAlexW4377011355

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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 OpenQuestion graph.