32 citations · 35 across the 4 of their papers we have counts for
5 papers
annbatch unlocks terabyte-scale training of biological data in anndata
Ilan Gold, Felix Fischer, Lucas Arnoldt +2
The scale of biological datasets now routinely exceeds system memory, making data access rather than model computation the primary bottleneck in training machine-learning models. T…
AI Scientists for Building Virtual-Cell Models
Xiangru Tang, Zhuoyun Yu, Jiapeng Chen +12
Autonomous agents, from virtual laboratories to biological data-analysis agents and co-scientists, have begun to turn scientific goals into hypotheses. Whether they can go further…
cp_measure: API-first feature extraction for image-based profiling workflows
Alán F. Muñoz, Tim Treis, Alexandr A. Kalinin +4
Biological image analysis has traditionally focused on measuring specific visual properties of interest for cells or other entities. A complementary paradigm gaining increasing tra…
How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Charlotte Bunne, Yusuf Roohani, Yanay Rosen +39
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determ…
To Transformers and Beyond: Large Language Models for the Genome
Micaela E. Consens, Cameron Dufault, Michael Wainberg +6
In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges. This review focuses on the transformative r…