ArticleJournal of chemical information and modeling2020
ivis Dimensionality Reduction Framework for Biomacromolecular Simulations.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- Structural Plasticity and Functional Dynamics of Pigeon Cryptochrome 4 as Avian Magnetoreceptor.Journal of molecular biology · 2025Article
- Optimization of drug-target affinity prediction methods through feature processing schemes.Bioinformatics (Oxford, England) · 2023Article
- Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials.The Journal of chemical physics · 2022Article
- Collective variable discovery in the age of machine learning: reality, hype and everything in between.RSC advances · 2022Review
- UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study.The journal of physical chemistry. B · 2021Article
- Explore Protein Conformational Space With Variational Autoencoder.Frontiers in molecular biosciences · 2021Article
- Deciphering the Allosteric Process of theThe journal of physical chemistry. B · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
Molecular dynamics (MD) simulations have been widely applied to study macromolecules including proteins. However, the high dimensionality of the data sets produced by simulations makes thorough analysis difficult and further hinders a deeper understanding of biomacromolecules. To gain more insights into the protein structure-function relations, appropriate dimensionality reduction methods are needed to project simulations onto low-dimensional spaces. Linear dimensionality reduction methods, such as principal component analysis (PCA) and time-structure-based independent component analysis (t-ICA), could not preserve sufficient structural information. Though better than linear methods, nonlinear methods, such as t-distributed stochastic neighbor embedding (t-SNE), still suffer from the limitations in avoiding system noise and keeping inter-cluster relations. ivis is a novel deep learning-based dimensionality reduction method originally developed for single-cell data sets. Here, we applied this framework for the study of light, oxygen, and voltage (LOV) domains of diatom
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