6 citations · 20 across the 6 of their papers we have counts for
6 papers
Autoregressive fragment-based diffusion for pocket-aware ligand design
Mahdi Ghorbani, Leo Gendelev, Paul Beroza +1
In this work, we introduce AutoFragDiff, a fragment-based autoregressive diffusion model for generating 3D molecular structures conditioned on target protein structures. We employ…
Learning Melanocytic Cell Masks from Adjacent Stained Tissue
Mikio Tada, Ursula E. Lang, Iwei Yeh +3
Melanoma is one of the most aggressive forms of skin cancer, causing a large proportion of skin cancer deaths. However, melanoma diagnoses by pathologists shows low interrater reli…
A single-cell gene expression language model
William Connell, Umair Khan, Michael J. Keiser
Gene regulation is a dynamic process that connects genotype and phenotype. Given the difficulty of physically mapping mammalian gene circuitry, we require new computational methods…
Robust Semantic Interpretability: Revisiting Concept Activation Vectors
Jacob Pfau, Albert T. Young, Jerome Wei +2
Interpretability methods for image classification assess model trustworthiness by attempting to expose whether the model is systematically biased or attending to the same cues as a…
Attention-Based Learning on Molecular Ensembles
Kangway V. Chuang, Michael J. Keiser
The three-dimensional shape and conformation of small-molecule ligands are critical for biomolecular recognition, yet encoding 3D geometry has not improved ligand-based virtual scr…
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias
Jacob Pfau, Albert T. Young, Maria L. Wei +1
In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons. In medical imaging, saliency maps h…