Evidence map›Paper›PMID 35529499›Full record

ArticlePeerJ2022

Network subgraph-based approach for analyzing and comparing molecular networks.

Chien-Hung Huang, Efendi Zaenudin, Jeffrey J P Tsai, Nilubon Kurubanjerdjit, Ka-Lok Ng

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

Article in PeerJ, 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
0.7field-weighted citation impact, top 34% 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, 8 citations in OpenAlex.

  1. Article
  2. Review
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 at 4 institutions in 3 countries.

Chien-Hung Huang *Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin, Taiwan.
Efendi ZaenudinNational Research and Innovation Agency, Bandung, Jawa Barat, Republic of Indonesia.
Jeffrey J P TsaiDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Nilubon KurubanjerdjitSchool of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.
Ka-Lok Ng *Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Asia University · TWMae Fah Luang University · THNational Formosa University · TWNational Nuclear Energy Agency of Indonesia · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen-Shannon entropy. We applied the subgraph approach to study three types of molecular networks,

Indexed as

AlgorithmsNeoplasmsHumansProteinsSignal TransductionProteinsCancer networksCellular processesEntropyGraph theoryInformation theoryJensen-Shannon entropyNetwork subgraphsSignal transduction networks

Identifiers

PMID35529499
PMCPMC9074881
OpenAlexW4225417845

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.