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Our paper "DeepGEEP: Data-Driven Prediction of Bacterial Biofilm Gene Expression Profiles" has been posted to bioRxiv. This paper considers petri dish experiments where a signaling molecule that can stimulate fluorescence is added to a freshly-plated bacteria sample. The fluorescence is triggered by gene expression and requires that the local signaling molecule concentration is within a suitable range. We try to predict where fluorescence will occur based on the location where the signaling molecules are dropped onto the dish. A convolutional neural network is applied to simulated data to make the predictions.

This paper is an output of the SIMBA project. It is first-authored by our group member Hamidreza Arjmandi and co-authored with Christophe Corre (University of Warwick School of Life Sciences and Department of Chemistry) and Hamidreza Jahangir.