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Creating Aesthetically Pleasing SBGN Visualisations for Presentation and Exploration

Czauderna, T.; Schreiber, F.

2023-12-23 systems biology
10.1101/2023.12.23.573191 bioRxiv
Show abstract

Visualisations are widely used to present, assess, and convey data and understanding in systems biology and other fields, and graphical representations of data and information should be precise, visually engaging, informative, and aesthetically pleasing. The Systems Biology Graphical Notation (SBGN) is a standard for visual representations of processes and networks in systems biology in the form of SBGN maps. It defines graphical elements and connection rules for knowledge representation in the form of maps. Whereas the form of the map elements is defined, those map elements have to be placed in a proper way in order to obtain useful and aesthetically pleasing visualisations. Here, we present layout considerations and guidelines for SBGN maps, as well as workflows for SBGN-ED as a tool for crafting visually pleasing SBGN visualisations for presentation and exploration.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 7%
22.9%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 2%
15.5%
Similar papers in this journal
3
BMC Bioinformatics
○ 457 papers in training set
Top 0.8%
8.1%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.4%
Similar papers in this journal
50% of probability mass above
5
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
4.1%
Similar papers in this journal
6
iScience
○ 1154 papers in training set
Top 9%
2.8%
7
Scientific Reports
○ 3612 papers in training set
Top 39%
2.5%
8
GigaScience
◐ 212 papers in training set
Top 2%
2.5%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 8%
2.0%
10
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.8%
1.8%
11
Gigabyte
● 62 papers in training set
Top 0.5%
1.8%
12
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 4%
1.5%
13
Journal of Molecular Biology
○ 232 papers in training set
Top 2%
1.5%
14
Biosystems
○ 31 papers in training set
Top 0.3%
1.4%
15
Neuroinformatics
○ 46 papers in training set
Top 0.6%
1.2%
16
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 0.7%
1.2%
17
Frontiers in Systems Biology
○ 10 papers in training set
Top 0.1%
1.2%
18
Biomolecules
○ 100 papers in training set
Top 2%
1.2%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.2%
20
Genome Biology
○ 637 papers in training set
Top 8%
1.1%
21
IEEE Access
● 35 papers in training set
Top 1%
0.9%
22
Frontiers in Physiology
○ 106 papers in training set
Top 2%
0.9%
23
Royal Society Open Science
● 214 papers in training set
Top 6%
0.9%
24
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 3%
0.6%
25
Nature Communications
○ 5641 papers in training set
Top 58%
0.6%
26
Nature Protocols
○ 33 papers in training set
Top 0.6%
0.6%
27
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.6%
28
eLife
● 5828 papers in training set
Top 69%
0.6%
29
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
30
ACS Synthetic Biology
● 287 papers in training set
Top 2%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.