deep-learning

A self-driving cell-biology lab where AI agents plan, run and adapt live-cell experiments across a cluster of instruments.

Building AI-powered data-driven human whole-cell models

An agent-first platform that deploys bioimage AI models as autoscaling, agent-readable interfaces on cloud or HPC — and powers the “test run” feature on the BioImage Model Zoo. Described in a 2026 preprint.

A community-driven, fully open repository where standardized deep-learning models can be shared, tested in the browser, and deployed across imaging tools.

Cell morphology, learned from image-based spatial proteomics, is shown to predict molecular phenotypes — bridging single-cell imaging and proteomics.

A collaborative community roadmap surveying how deep learning is applied across microscopy — image restoration, detection, segmentation, classification, tracking, and multimodal fusion — with its possibilities and limitations.

Large-scale immunofluorescence profiling maps how SARS-CoV-2 reshuffles the host protein landscape across 602 proteins, then screens approved drugs against the changed targets — yielding a 100,000-image public resource and pinpointing elesclomol as an antiviral modulator.

A universal deep-learning approach for accurate FISH spot detection across imaging-based spatial-omics datasets.

A framework that makes deep-learning pipelines for microscopy flexible, shareable, and reproducible across computing environments.

We’re excited to welcome Nils Mechtel as our second PhD student at AICell Lab. His expertise in computational biology and deep learning will play a crucial role in advancing our human cell simulator project.