ArticleJACS Au2026
CLaSSiNet: A Computational Framework for High-Resolution Classification and Spatial Mapping of Heterogeneous Biological Network Architectures.
Article in JACS Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Quantitative analysis of network-like biological molecular architectures, such as cytoskeletal networks, remains a fundamental challenge in super-resolution fluorescence imaging because single-molecule localization microscopy (SMLM) typically produces sparse and discontinuous localization patterns arising from stochastic labeling, incomplete probe occupancy, and structural deformation of biological networks. These factors obscure the underlying connectivity, periodicity, and symmetry, limiting the ability of existing analysis methods to resolve higher-order organization. Here, we report the Classifier of Super-resolution Structural Networks (
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Registered trials
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.