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Graph Lens Lite: An interactive biological network viewer for displaying, exploring, and sharing disease pathobiology and drug mechanism of action models

Ley, M.; Keska-Izworska, K.; Fillinger, L.; Walter, S. M.; Baumgärtel, F.; Bono, E.; Galou, L.; Andorfer, P.; Hauser, P.; Leierer, J.; kratochwill, k.; Perco, P.

2026-02-27 bioinformatics
10.64898/2026.02.25.708026 bioRxiv
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MotivationBiological network visualization together with graph-based analyses are key techniques in systems biology and network medicine to detect patterns and generate new hypotheses regarding disease pathobiology, drug target identification, or digital drug discovery. Network representations are also a way to communicate research findings and share results with colleagues and coworkers. ResultsWe have developed Graph Lens Lite, a browser-based tool that combines rich visualization capabilities with a streamlined interface for exploring and sharing biological networks. It offers an expressive query language, topological network analysis, GUI-based filtering, visual grouping, customizable layouts, a data-editor, and fine-grained property-based styling options, particularly suited for visualizing molecular models of disease pathobiology or drug mechanism of action. AvailabilityGraph Lens Lite is available at GitHub (https://github.com/Delta4AI/GraphLensLite). Supplementary informationSupplementary data are available at Bioinformatics online.

Matching journals

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

1
Bioinformatics
◐ 1204 papers in training set
Top 0.6%
30.5%
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2
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Top 22%
7.7%
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3
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Top 0.3%
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Top 7%
5.4%
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50% of probability mass above
5
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Top 2%
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Top 1%
5.1%
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● 25 papers in training set
Top 0.1%
3.2%
8
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● 242 papers in training set
Top 2%
2.7%
9
Journal of Molecular Biology
○ 232 papers in training set
Top 1%
2.3%
10
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◐ 61 papers in training set
Top 0.4%
2.1%
11
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○ 15 papers in training set
Top 0.1%
2.1%
12
Frontiers in Genetics
○ 230 papers in training set
Top 3%
1.7%
13
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Top 55%
1.7%
14
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○ 15 papers in training set
Top 0.1%
1.5%
15
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○ 49 papers in training set
Top 0.7%
1.4%
16
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Top 0.9%
1.1%
17
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Top 0.8%
1.0%
18
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.7%
0.8%
19
iScience
○ 1154 papers in training set
Top 37%
0.8%
20
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 2%
0.8%
21
eLife
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Top 67%
0.8%
22
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Top 13%
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"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.