Frimpong Boadu

This paper introduces FunBind, a multimodal model for protein function prediction that improves accuracy and enables zero-shot prediction of novel functions.

MPBind predicts protein binding sites for multiple partners using protein language models and equivariant graph neural networks, outperforming existing methods.

The paper uses multi-omics data and machine learning to analyze oviductal responses to gametes and embryos in mice, identifying region-specific transcriptional and proteomic changes.

The paper introduces TransFew, a transformer model that integrates protein sequence and function label representations to improve protein function prediction, especially for rare function terms.

This study identifies molecular subgroups and epigenetic alterations in Beckwith-Wiedemann Syndrome and large offspring syndrome across methylome, transcriptome, and chromatin architecture.

This paper introduces TransFun, a method that combines protein sequences and structures using transformers and equivariant graph neural networks to improve protein function prediction.