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Found 45 papers, 12 papers with code
Drug resistance revealed by in silico deep mutational scanning and mutation tracker
As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness.
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics.
Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing
This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery.
Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2.
Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization
Single-cell RNA sequencing (scRNA-seq) is a relatively new technology that has stimulated enormous interest in statistics, data science, and computational biology due to the high dimensionality, complexity, and large scale associated with scRNA-seq data.
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis
We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method.
ChatGPT for Computational Topology
This work serves as an initial step towards effectively transforming pure mathematical theories into practical computational tools, with the ultimate goal of enabling real applications across diverse fields.
ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots
This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more.
Machine Learning Study of the Extended Drug-target Interaction Network informed by Pain Related Voltage-Gated Sodium Channels
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 addiction.
Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex
Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions.
Mathematics-assisted directed evolution and protein engineering
Persistent Laplacians, an emerging technique in topological data analysis (TDA), have made structure-based embeddings a superb option in AIDE and AIPE.
Persistent Laplacian-enhanced Algorithm for Scarcely Labeled Data Classification
Overall, it is a very efficient procedure that requires much less labeled data to perform well compared to many ML techniques, and it can be adapted for both small and large datasets.
Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder
Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors.
Persistent topological Laplacian analysis of SARS-CoV-2 variants
Topological data analysis (TDA) is an emerging field in mathematics and data science.
Machine-learning Analysis of Opioid Use Disorder Informed by MOR, DOR, KOR, NOR and ZOR-Based Interactome Networks
Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments.
SVSBI: Sequence-based virtual screening of biomolecular interactions
Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery.
Integrating Transformer and Autoencoder Techniques with Spectral Graph Algorithms for the Prediction of Scarcely Labeled Molecular Data
In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints.
Emerging dominant SARS-CoV-2 variants
Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections.
Topological AI forecasting of future dominating viral variants
AI-based forecasting of Omicron's infectivity, vaccine breakthrough, and antibody resistance was later nearly perfectly confirmed by experiments.
CCP: Correlated Clustering and Projection for Dimensionality Reduction
Most dimensionality reduction methods employ frequency domain representations obtained from matrix diagonalization and may not be efficient for large datasets with relatively high intrinsic dimensions.
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
In particular, BA. 4 and BA. 5 are about 36\% more infectious than BA. 2 and are projected to become new dominating variants by natural selection.
Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies
It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2.
Omicron BA.2 (B.1.1.529.2): high potential to becoming the next dominating variant
The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability.
Machine learning analysis of cocaine addiction informed by DAT, SERT, and NET-based interactome networks
Cocaine dependence is neurological and involves many interacting proteins in the interactome.
Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance
The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B. 1. 1. 529) has ushered panic responses around the world due to its contagious and vaccine escape mutations.
The evolution of the mechanisms of SARS-CoV-2 evolution revealing vaccine-resistant mutations in Europe and America
The importance of understanding SARS-CoV-2 evolution cannot be overemphasized.
MLIMC: Machine learning-based implicit-solvent Monte Carlo
Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions.
Proteome-informed machine learning studies of cocaine addiction
Cocaine addiction accounts for a large portion of substance use disorders and threatens millions of lives worldwide.
Review of the mechanisms of SARS-CoV-2 evolution and transmission
We anticipate that viral evolution will combine RBD co-mutations at these two sites, creating future variants that are tens of times more infectious than the original SARS-CoV-2.
Emerging vaccine-breakthrough SARS-CoV-2 variants
The molecular mechanism underlying such surge is elusive due to 4, 653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines.
Multiscale Laplacian Learning
In particular, the performance of machine learning methods is often severely affected in case of diverse data, usually associated with smaller data sets or data related to areas of study where the size of the data sets is constrained by the complexity and/or high cost of experiments.
Vaccine-escape and fast-growing mutations in the United Kingdom, the United States, Singapore, Spain, South Africa, and other COVID-19-devastated countries
We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants can potentially disrupt the binding between the RDB and many known antibodies.
Methodology-centered review of molecular modeling, simulation, and prediction of SARS-CoV-2
To provide the reader a quick update about the status of molecular modeling, simulation, and prediction of SARS-CoV-2, we present a comprehensive and systematic methodology-centered narrative in the nick of time.
Host immune response driving SARS-CoV-2 evolution
Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreacting to the viral infection.
Characterizing SARS-CoV-2 mutations in the United States
Using genotyping, sequence-alignment, time-evolution, $k$-means clustering, protein-folding stability, algebraic topology, and network theory, we reveal that the US SARS-CoV-2 has four substrains and five top US SARS-CoV-2 mutations were first detected in China (2 cases), Singapore (2 cases), and the United Kingdom (1 case).
Decoding asymptomatic COVID-19 infection and transmission
By analyzing the distribution of 11083G>T in various countries, we unveil that 11083G>T may correlate with the hypotoxicity of SARS-CoV-2.
Generative network complex for the automated generation of druglike molecules
Current drug discovery is expensive and time-consuming.
Biomolecules Quantitative Methods
Mutations on COVID-19 diagnostic targets
Effective, sensitive, and reliable diagnostic reagents are of paramount importance for combating the ongoing coronavirus disease 2019 (COVID-19) pandemic at a time there is no preventive vaccine nor specific drug available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Decoding SARS-CoV-2 transmission, evolution and ramification on COVID-19 diagnosis, vaccine, and medicine
Tremendous effort has been given to the development of diagnostic tests, preventive vaccines, and therapeutic medicines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Mathematics at the eve of a historic transition in biology
A century ago physicists and mathematicians worked in tandem and established quantum mechanism.
Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity prediction
Toxicity analysis and prediction are of paramount importance to human health and environmental protection.
Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies
The underpinning assumptions of FFT-BP are as follows: i) representability: there exists a microscopic feature vector that can uniquely characterize and distinguish one protein-ligand complex from another; ii) feature-function relationship: the macroscopic features, including binding free energy, of a complex is a functional of microscopic feature vectors; and iii) similarity: molecules with similar microscopic features have similar macroscopic features, such as binding affinity.
A topological approach for protein classification
However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology.
Biomolecules
