34 citations · 49 across the 14 of their papers we have counts for
15 papers
Persistent Sheaf Laplacian Analysis of Protein Stability and Solubility Changes upon Mutation
Yiming Ren, Junjie Wee, Xi Chen +2
Genetic mutations frequently disrupt protein structure, stability, and solubility, acting as primary drivers for a wide spectrum of diseases. Despite the critical importance of the…
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…
Commutative algebra neural network reveals genetic origins of diseases
JunJie Wee, Faisal Suwayyid, Mushal Zia +3
Genetic mutations can disrupt protein structure, stability, and solubility, contributing to a wide range of diseases. Existing predictive models often lack interpretability and fai…
A review of topological data analysis and topological deep learning in molecular sciences
JunJie Wee, Jian Jiang
Topological Data Analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelli…
CAKR: Commutative algebra k-mer representations for genomics
Faisal Suwayyid, Yuta Hozumi, Mushal Zia +3
Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fund…
Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Xiang Liu, Junjie Wee, Guo-Wei Wei
Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are l…
CAML: Commutative algebra machine learning -- a case study on protein-ligand binding affinity prediction
Hongsong Feng, Faisal Suwayyid, Mushal Zia +4
Recently, Suwayyid and Wei have introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we integrate commutative algebra machine…
Rapid response to fast viral evolution using AlphaFold 3-assisted topological deep learning
JunJie Wee, Guo-Wei Wei
The fast evolution of SARS-CoV-2 and other infectious viruses poses a grand challenge to the rapid response in terms of viral tracking, diagnostics, and design and manufacture of m…
A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity
JunJie Wee, Xue Gong, Wilderich Tuschmann +1
We introduce, for the first time, a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voi…
Topology-enhanced machine learning model (Top-ML) for anticancer peptide prediction
Joshua Zhi En Tan, JunJie Wee, Xue Gong +1
Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have bee…
Benchmarking AlphaFold3's protein-protein complex accuracy and machine learning prediction reliability for binding free energy changes upon mutation
JunJie Wee, Guo-Wei Wei
AlphaFold 3 (AF3), the latest version of protein structure prediction software, goes beyond its predecessors by predicting protein-protein complexes. It could revolutionize drug di…
Drug Resistance Predictions Based on a Directed Flag Transformer
Dong Chen, Gengzhuo Liu, Hongyan Du +6
The continuous evolution of the SARS-CoV-2 virus poses a significant challenge to global public health. Of particular concern is the potential resistance to the widely prescribed d…
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…
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…
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…