Kai-Wei Chang

The paper introduces a Transformer-based model for annotating Gene Ontology terms in protein sequences, showing improved accuracy over CNN-based methods and incorporating additional features like BLAST motifs and structural data.

This paper evaluates neural network models for embedding Gene Ontology terms and introduces new models based on GCN, ELMo, and BERT, finding that BERT-based embeddings perform best for rare labels.

Lasagna is an end-to-end framework for protein-protein interaction prediction using primary sequences, outperforming existing methods in binary prediction and showing promise in interaction type and binding affinity tasks.

The paper introduces two NLP-based methods to measure semantic similarity of Gene Ontology terms using their definitions, achieving better classification accuracy than tree-based methods.