Deep learning–based high-information-content graph representation of early stage bacterial biofilms
An AI-driven computational framework for high-resolution analysis of early-stage bacterial biofilms. The system integrates deep learning, microscopy, and graph-based modeling to uncover non-obvious patterns of microbial organization.
Launch application →Automated cell segmentation using Mask R-CNN combined with a custom neural network (BINet) for intercellular interaction prediction.
Representation of biofilms as undirected interaction graphs, where cells are vertices and predicted interactions are edges.
Extraction of graph features for studying biofilm growth, identifying recurrent structural motifs, and classifying colonization patterns.
Prediction of developmental stage and substrate material type directly from image-derived graph representations.
Bacterial biofilms are spatially organized microbial communities that exhibit increased resistance to antibiotics and play a key role in chronic infections. Understanding their structure at early formation stages is essential for developing effective intervention strategies.
Biofilms2Graph provides a scalable, high-information-content framework for automated biofilm analysis. By combining microscopy visualization with deep learning and graph theory, the platform enables systems-level exploration of microbial community organization and reveals previously hidden structural patterns.
Authors: Lev E. Nersesyan, Daniil A. Boiko, Saniyat Kurbanalieva, Lilya U. Dzhemileva, Konstantin S. Kozlov, Valentine P. Ananikov
Title: Deep learning-based high-information-content graph representation of early stage bacterial biofilms
Year: 2026 | Version: 1.0
Access the full-featured AI tool for automated biofilm image analysis and graph construction.
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