Evidence map›Paper›PMID 30174525›Full record

ArticleSaudi journal of biological sciences2018

Study of biological networks using graph theory.

Wei Gao, Hualong Wu, Muhammad Kamran Siddiqui, Abdul Qudair Baig

Abstract read
In one paragraph

Article in Saudi journal of biological sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Biochemical and phylogenetic networks-II:Journal of mathematical chemistry · 2021
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. 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

4 authors.

Wei GaoSchool of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Hualong WuSchool of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Muhammad Kamran SiddiquiDepartment of Mathematics, Comsats Institute of Information Technology, Sahiwal 57000, Pakistan.
Abdul Qudair BaigDepartment of Mathematics, Comsats Institute of Information Technology, Attock Campus, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, gene or protein can be denoted as a vertex, and the connect element can be regarded as an edge. In this way, the biological activity characteristic can be measured via topological index computing in the corresponding graphs. In our article, we mainly study the biology features of biological networks in terms of eccentric topological indices computation. By means of graph structure analysis and distance calculating, the exact expression of several important eccentric related indices of hypertree network and

Indexed as

Biological mathematicsBiological networksDNA sequenceTopological index

Identifiers

PMID30174525
PMCPMC6117245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.