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arXiv · 2609.15236

ProLiVis 2.0: Literature-Centric Visualization of Protein--Protein Interaction Networks, with a Citation-Trust Model for Interaction Evidence

Melih Sözdinler·Yalçın Doksanbir·Gökhan Akpınar·Ege Aktan
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Abstract

Protein-protein interaction databases record evidence without weighing it. In BioGRID, an interaction asserted once by a single high-throughput screen and one confirmed by twenty laboratories across a dozen assays are the same kind of row in the same file. Tools built on such databases inherit that flattening: they draw every reported interaction as an edge, and the resulting picture states that two proteins interact without stating how much anyone should believe it. We present ProLiVis 2.0, a rewrite of the literature-centric visualization system of arXiv:2111.12794. It contributes three things. First, a citation-trust model that scores each interaction from seven terms, including a term for the number of independent laboratories behind the supporting publications, obtained by clustering those publications over shared institutional affiliations; a plain count of publications cannot distinguish five confirmations from one group publishing five times. Second, a deterministic reformulation of the center layout, closed-form and $O(n \log n)$, which replaces the force-directed placement of the original and makes published figures regenerable from a session manifest. Third, an implementation that runs entirely in a web browser, with an embedded analytical database, requiring no installation and uploading no data. On BioGRID release 5.0.260 restricted to SARS-CoV-2, 24,344 of 34,540 reported interactions (70%) rest on a single publication, and raising the trust threshold to 0.2 leaves 11,320 of them. That the large majority of a curated interaction network is unreplicated is a fact no existing view of the database makes visible.

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Melih Sözdinler, Yalçın Doksanbir, Gökhan Akpınar, Ege Aktan. 2026-09-14. ProLiVis 2.0: Literature-Centric Visualization of Protein--Protein Interaction Networks, with a Citation-Trust Model for Interaction Evidence. https://arxiv.org/abs/2609.15236

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