34 citations · 36 across the 4 of their papers we have counts for
6 papers
JASPR: Joint Spatial Representation learning of histology and spatial genomics for improved virtual genomic screening and clinical prognostication
Marija Pizurica, Eric Zimmermann, Neil Tenenholtz +5
Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial properties are increasingl…
Multimodal Alignment Improves Generalizability of Genomic Biomarker Prediction in Computational Pathology
Ekaterina Redekop, Eric Zimmermann, Ava P Amini +5
Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for t…
Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling
Yemin Yu, Emre Hayir, Neil Tenenholtz +5
Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput microscopy screens, a process known as morp…
Towards deep learning sequence-structure co-generation for protein design
Chentong Wang, Sarah Alamdari, Carles Domingo-Enrich +2
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majorit…
Continuous Time Evidential Distributions for Irregular Time Series
Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen +1
Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from. It is difficult to infer the value of a feature at an…
Protein structure generation via folding diffusion
Kevin E. Wu, Kevin K. Yang, Rianne van den Berg +3
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable disease…