Evidence map›Paper›PMID 30367584›Full record

ArticleBMC bioinformatics2018

ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species.

Jianliang Gao, Bo Song, Xiaohua Hu, Fengxia Yan, Jianxin Wang

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. 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
–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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Jianliang GaoSchool of Information Science and Engineering, Central South University, Changsha, 410083, China. gaojianliang@csu.edu.cn.
Bo SongCollege of Computing & Informatics, Drexel University, Philadelphia, 19104, USA.
Xiaohua HuCollege of Computing & Informatics, Drexel University, Philadelphia, 19104, USA.
Fengxia YanCollege of Liberal Arts and Sciences, National University of Defence Technology, Changsha, 410073, China.
Jianxin WangSchool of Information Science and Engineering, Central South University, Changsha, 410083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes.

resultsIn this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species.

conclusionsWe conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species.

Indexed as

AlgorithmsAnimalsCaenorhabditis elegansComputational BiologyDrosophilaHumansProtein Interaction MappingProteinsSaccharomyces cerevisiaeSpecies SpecificityProteinsBig dataGraph data analysisNetwork alignment

Identifiers

PMID30367584
PMCPMC6101090

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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.