34 citations · 49 across the 14 of their papers we have counts for
6 papers · 1 filter
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…