Oz Kilim

This paper uses open-weight large language models to predict synthetic lethal interactions, outperforming random chance and non-LLM methods in reproducing CRISPR screen results.

This study combines histopathology and proteomics with a multimodal deep learning framework to improve platinum response prediction in HGSOC, outperforming HRD scores.

The paper analyzes the bacteriome composition of Ixodes ricinus ticks across different climatic conditions and developmental stages, finding significant differences in bacterial genera between females and nymphs.

This study evaluates how deep learning models predict dairy cow body condition scores using image annotations, finding stronger agreement with fewer classes and larger error ranges.