36 citations · 48 across the 6 of their papers we have counts for
6 papers
SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval
Hamed Shirzad, Frederik Wenkel, Dominique Beaini +2
Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph expansion grows rapidly, and dense…
Towards Autonomous Mechanistic Reasoning in Virtual Cells
Yunhui Jang, Lu Zhu, Jake Fawkes +3
Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scienti…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval
Philip Fradkin, Puria Azadi, Karush Suri +4
Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based…
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +14
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores…
On the Scalability of GNNs for Molecular Graphs
Maciej Sypetkowski, Frederik Wenkel, Farimah Poursafaei +4
Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model…