20 citations · 37 across the 9 of their papers we have counts for
10 papers
Generalising sequence models for epigenome predictions with tissue and assay embeddings
Jacob Deasy, Ron Schwessinger, Ferran Gonzalez +2
Sequence modelling approaches for epigenetic profile prediction have recently expanded in terms of sequence length, model size, and profile diversity. However, current models canno…
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation
Cuong Q. Nguyen, Dante Pertusi, Kim M. Branson
Image-based profiling techniques have become increasingly popular over the past decade for their applications in target identification, mechanism-of-action inference, and assay dev…
Deep reinforced active learning for multi-class image classification
Emma Slade, Kim M. Branson
High accuracy medical image classification can be limited by the costs of acquiring more data as well as the time and expertise needed to label existing images. In this paper, we a…
Epigenomic language models powered by Cerebras
Meredith V. Trotter, Cuong Q. Nguyen, Stephen Young +2
Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biologic…
All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption chara…
ALBRT: Cellular Composition Prediction in Routine Histology Images
Muhammad Dawood, Kim Branson, Nasir M. Rajpoot +1
Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosi…