Kai Kang

PepMCP predicts membrane contact probability for short peptides using graph-based modeling, achieving high correlation with experimental data.

The paper introduces ProtRAP-LM, a transformer-based model using language model embeddings to predict membrane contact probability and relative accessibility of proteins, achieving faster and more accurate results than MSA-based methods.

This paper introduces ONN4MST, a deep learning method for microbial source tracking that achieves high accuracy and speed across large datasets of microbial communities.