Allen Goodman

Property Enhancer optimizes antibody properties using a data-efficient framework, achieving significant affinity gains in both in silico and in vitro tests.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

This paper trains a protein language model in 24 GPU hours, achieving performance comparable to a much larger model trained for 15,000x more GPU hours.

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

This study uses deep learning and label-free imaging flow cytometry to classify red blood cell morphologies during storage, achieving 90% accuracy and predicting expiration dates based on morphological changes.

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.

This study evaluates deep learning strategies for nucleus segmentation in fluorescence images, showing U-Net outperforms classical methods and reduces biologically relevant errors.

This paper develops a weakly supervised CNN approach for learning single-cell feature embeddings from microscopy images, achieving better accuracy than classical features and competitiveness with transfer learning methods.