ArticleJournal of chemical theory and computation2018
t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations.
Article in Journal of chemical theory and computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
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- NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks.Journal of chemical information and modeling · 2025Article
- Machine learning-based integration develops a hypoxia-derived signature for improving outcomes in glioma.iScience · 2025Article
- Improving radiomics-based models for esophagogastric variceal bleeding risk prediction in cirrhotic patients.World journal of gastroenterology · 2025Article
- Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset.bioRxiv : the preprint server for biology · 2025Article
- TRIM44, a Novel Prognostic Marker, Supports the Survival of Proteasome-Resistant Multiple Myeloma Cells.Cells · 2024Article
- An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue.Scientific reports · 2024Article
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.Journal of chemical information and modeling · 2024Article
- Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.The journal of physical chemistry. B · 2024Article
- Dynamic changes of immunocyte subpopulations in thermogenic activation of adipose tissues.Frontiers in immunology · 2024Article
- Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma.Scientific reports · 2023Article
- Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping.Nutrients · 2023Article
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.bioRxiv : the preprint server for biology · 2023Article
- Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding.Journal of chemical theory and computation · 2023Article
- Identification of N7-methylguanosine-related lncRNAs for the risk stratification of hepatocellular carcinoma.Journal of gastrointestinal oncology · 2023Article
- Density Functional Theory Computation and Machine Learning Studies of Interaction between AuACS omega · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Dimensionality reduction methods are usually applied on molecular dynamics simulations of macromolecules for analysis and visualization purposes. It is normally desired that suitable dimensionality reduction methods could clearly distinguish functionally important states with different conformations for the systems of interest. However, common dimensionality reduction methods for macromolecules simulations, including predefined order parameters and collective variables (CVs), principal component analysis (PCA), and time-structure based independent component analysis (t-ICA), only have limited success due to significant key structural information loss. Here, we introduced the t-distributed stochastic neighbor embedding (t-SNE) method as a dimensionality reduction method with minimum structural information loss widely used in bioinformatics for analyses of macromolecules, especially biomacromolecules simulations. It is demonstrated that both one-dimensional (1D) and two-dimensional (2D) models of the t-SNE method are superior to distinguish important functional states of a model allosteric protein system for free energy and mechanistic analysis. Projections of the model protein simulations onto 1D and 2D t-SNE surfaces provide both clear visual cues and quantitative information, which is not readily available using other methods, regarding the transition mechanism between two important functional states of this protein.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.