Maxwell L. Bileschi

The paper maps the genome packaging landscape of AAV2 capsid protein using high-throughput synthesis and machine learning, revealing navigable subspaces of functionally viable sequences.

ProtEx improves protein function prediction by using retrieved exemplars, achieving state-of-the-art results for EC numbers, GO terms, and Pfam families.

ProteInfer uses deep convolutional neural networks to predict protein functions from amino acid sequences, offering efficient and accurate predictions with a lightweight software interface.

This paper uses deep learning to improve protein function annotation by training on Pfam data, achieving better accuracy and efficiency than BLASTp.

The paper critiques protein family classification models by identifying sufficient input subsets to understand their decision-making criteria.

The paper develops a machine learning system using convolutional neural networks to predict cell type-specific epigenetic and transcriptional profiles from DNA sequence, showing alignment with eQTL causal variants.