12 papers
Commutative Algebra Learning for Protein Flexibility Analysis
Honghao Zhang, Hongsong Feng
Protein flexibility, commonly quantified by B-factors, is closely related to protein structure and function. However, accurate B-factor prediction remains challenging due to the mu…
Persistent local Laplacian prediction of protein-ligand binding affinities
Jian Liu, Hongsong Feng
Accurate prediction of protein-ligand binding affinity remains a central challenge in structure-based drug discovery. The effectiveness of machine learning models critically depend…
Local Laplacian: theory and models for data analysis
Jian Liu, Hongsong Feng, Kefeng Liu
While topological data analysis has emerged as a powerful paradigm for structural inference, its foundational tools, notably persistent homology and the persistent Laplacian, are f…
Persistent magnitude homology on finite metric space
Wanying Bi, Hongsong Feng, Jingyan Li +1
Magnitude homology is an emerging approach that captures the intrinsic topological and geometric features of metric spaces. It offers a distinct theoretical lens for interpreting s…
Commutative Algebra Modeling in Materials Science -- A Case Study on Metal-Organic Frameworks (MOFs)
Caleb Simiyu Khaemba, Hongsong Feng, Dong Chen +2
Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in mater…
CAP: Commutative Algebra Prediction of Protein-Nucleic Acid Binding Affinities
Mushal Zia, Faisal Suwayyid, Yuta Hozumi +3
An accurate prediction of protein-nucleic acid binding affinity is vital for deciphering genomic processes, yet existing approaches often struggle in reconciling high accuracy with…