1 citations · 1 across the 18 of their papers we have counts for
24 papers
Protein B-factor modeling with local magnitude
Wanying Bi, Jian Liu, Hongsong Feng
Topological magnitude characterizes the effective size and spatial organization of metric spaces, offering a geometric approach with potential applications in molecular biology and…
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…
Commutative Algebra Modeling of Mutation and Disease Association
JunJie Wee, Faisal Suwayyid, Mushal Zia +3
Genetic mutations can lead to disease by disrupting molecular interactions, altering protein stability, inducing misfolding, and promoting protein aggregation. Although existing pr…
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…
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…
Persistent Sheaf Laplacian Analysis of Protein Flexibility
Nicole Hayes, Xiaoqi Wei, Hongsong Feng +2
Protein flexibility, measured by the B-factor or Debye-Waller factor, is essential for protein functions such as structural support, enzyme activity, cellular communication, and mo…
Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction
Mushal Zia, Benjamin Jones, Hongsong Feng +1
Directionality in molecular and biomolecular networks plays a significant role in the accurate represention of the complex, dynamic, and asymmetrical nature of interactions present…
Multiscale differential geometry learning for protein flexibility analysis
Hongsong Feng, Jeffrey Y. Zhao, Guo-Wei Wei
Protein flexibility is crucial for understanding protein structures, functions, and dynamics, and it can be measured through experimental methods such as X-ray crystallography. The…
Mayer-homology learning prediction of protein-ligand binding affinities
Hongsong Feng, Li Shen, Jian Liu +1
Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and…
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Hongsong Feng, Sean Cottrell, Yuta Hozumi +1
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expres…
Knot data analysis using multiscale Gauss link integral
Li Shen, Hongsong Feng, Fengling Li +3
In the past decade, topological data analysis (TDA) has emerged as a powerful approach in data science. The main technique in TDA is persistent homology, which tracks topological i…
ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots
Rui Wang, Hongsong Feng, Guo-Wei Wei
The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientif…
Machine Learning Study of the Extended Drug-target Interaction Network informed by Pain Related Voltage-Gated Sodium Channels
Long Chen, Jian Jiang, Bozheng Dou +6
Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addicti…
Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex
Hongsong Feng, Rui Wang, Chang-Guo Zhan +1
Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions. Due to the lack of effective treatment options for various…
Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder
Hongsong Feng, Jian Jiang, Guo-Wei Wei
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medication…
Machine-learning Analysis of Opioid Use Disorder Informed by MOR, DOR, KOR, NOR and ZOR-Based Interactome Networks
Hongsong Feng, Rana Elladki, Jian Jiang +1
Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. De…
SVSBI: Sequence-based virtual screening of biomolecular interactions
Li Shen, Hongsong Feng, Yuchi Qiu +1
Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally…
TIDAL: Topology-Inferred Drug Addiction Learning
Zhu Zailiang, Dou Bozheng, Cao Yukang +8
Drug addiction or drug overdose is a global public health crisis, and the design of anti-addiction drugs remains a major challenge due to intricate mechanisms. Since experimental d…
Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks
Hongsong Feng, Kaifu Gao, Dong Chen +3
Cocaine addiction is a psychosocial disorder induced by the chronic use of cocaine and causes a large of number deaths around the world. Despite many decades' effort, no drugs have…