1 citations · 1 across the 2 of their papers we have counts for
5 papers
Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparities
Michal Rosen-Zvi, Yoav Kan-Tor, Michael Danziger +6
Healthcare disparities persist across socioeconomic boundaries, often attributed to unequal access to screening, diagnostics, and therapeutics. However, this perspective highlights…
BMFM-RNA: whole-cell expression decoding improves transcriptomic foundation models
Michael M. Danziger, Bharath Dandala, Viatcheslav Gurev +12
Transcriptomic foundation models pretrained with masked language modeling can achieve low pretraining loss yet produce poor cell representations for downstream tasks. We introduce…
BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Hongyang Li, Sanjoy Dey, Bum Chul Kwon +7
Large language models (LLMs) trained on text demonstrated remarkable results on natural language processing (NLP) tasks. These models have been adapted to decipher the language of…
MAMMAL -- Molecular Aligned Multi-Modal Architecture and Language
Yoel Shoshan, Moshiko Raboh, Michal Ozery-Flato +18
Large language models applied to vast biological datasets have the potential to transform biology by uncovering disease mechanisms and accelerating drug development. However, curre…
Does your model understand genes? A benchmark of gene properties for biological and text models
Yoav Kan-Tor, Michael Morris Danziger, Eden Zohar +2
The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying…