259 citations · 631 across the 36 of their papers we have counts for
37 papers
Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
JunJie Wee, Jiahui Chen, Kelin Xia +1
Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine le…
Torsion Graph Neural Networks
Cong Shen, Xiang Liu, Jiawei Luo +1
Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological inf…
Molecular geometric deep learning
Cong Shen, Jiawei Luo, Kelin Xia
Geometric deep learning (GDL) has demonstrated huge power and enormous potential in molecular data analysis. However, a great challenge still remains for highly efficient molecular…
Persistent Dirac for molecular representation
JunJie Wee, Ginestra Bianconi, Kelin Xia
Molecular representations are of fundamental importance for the modeling and analysis of molecular systems. Representation models and in general approaches based on topological dat…
A Unified Topological Approach to Data Science
Jelena Grbić, Jie Wu, Kelin Xia +1
We establish a new theory which gives a unified topological approach to data science, by being applicable both to point cloud data and to graph data, including networks beyond pair…
Ollivier persistent Ricci curvature (OPRC) based molecular representation for drug design
JunJie Wee, Kelin Xia
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here we propose persistent Ricci curvature (PRC), in particular Ollivier pe…