Daniel W. A. Buchan

TEDlm is a domain-centric protein language model that improves remote-homology detection and Molecular Function prediction by pretraining on structurally-defined domain sequences.

Hybrid Gated Fusion improves protein function annotation by integrating sequence, structure, text, and interaction data using bilinear gating and auxiliary supervision, achieving state-of-the-art results on CAFA3.

This paper introduces GAN-RCL, a contrastive learning method for cell-type identification in single-cell RNA-Seq that outperforms existing methods.

This study evaluates contrastive learning generative networks for estimating cell-type distributions in single-cell RNA-Seq data, finding supervised methods outperform self-supervised ones.

The paper analyzes protein search drift in iterative searches and shows its impact on structure prediction using Alphafold2.

This study uses variational autoencoders to generate minimal E. coli genomes, finding 6 out of 100 sampled genomes were viable in a computational model.

This paper introduces a contrastive learning framework with Gaussian noise augmentation for improved cell-type prediction in scRNA-seq data.

The paper uses word2vec to create semantically meaningful embeddings of protein domains and applies them to identify functional roles of unknown domains.