35 citations · 56 across the 7 of their papers we have counts for
7 papers
Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin
Ella Barkan, Ibrahim Siddiqui, Kevin J. Cheng +6
Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved modalities for treating autoimmune diseases, infectious diseases, and cancers. However, discovery and…
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…
A large dataset curation and benchmark for drug target interaction
Alex Golts, Vadim Ratner, Yoel Shoshan +7
Bioactivity data plays a key role in drug discovery and repurposing. The resource-demanding nature of \textit{in vitro} and \textit{in vivo} experiments, as well as the recent adva…
The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT
Nicholas Heller, Fabian Isensee, Dasha Trofimova +42
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical…
Regularized adversarial examples for model interpretability
Yoel Shoshan, Vadim Ratner
As machine learning algorithms continue to improve, there is an increasing need for explaining why a model produces a certain prediction for a certain input. In recent years, sever…
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
Vadim Ratner, Yoel Shoshan, Tal Kachman
Medical image classification involves thresholding of labels that represent malignancy risk levels. Usually, a task defines a single threshold, and when developing computer-aided d…
AdapterNet - learning input transformation for domain adaptation
Alon Hazan, Yoel Shoshan, Daniel Khapun +2
Deep neural networks have demonstrated impressive performance in various machine learning tasks. However, they are notoriously sensitive to changes in data distribution. Often, eve…