ArticleThe Journal of chemical physics2020
Inferring effective forces for Langevin dynamics using Gaussian processes.
Article in The Journal of chemical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Stochastic colonization and host-to-host transmission shape gut bacterial variability.bioRxiv : the preprint server for biology · 2026Article
- Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Harnessing non-equilibrium forces to optimize work extraction.Nature communications · 2025Article
- A cautious user's guide in applying HMMs to physical systems.The Journal of chemical physics · 2025Article
- Mamba time series forecasting with uncertainty quantification.Machine learning: science and technology · 2025Article
- Article
- Simultaneous particle tracking, phase retrieval and point spread function reconstruction.bioRxiv : the preprint server for biology · 2025Article
- DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes.Biophysical reports · 2025Article
- Learning Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning.bioRxiv : the preprint server for biology · 2024Article
- Building Fluorescence Lifetime Maps Photon-by-Photon by Leveraging Spatial Correlations.ACS photonics · 2023Article
- Article
- Single-photon smFRET. I: Theory and conceptual basis.Biophysical reports · 2023Article
- Learning continuous potentials from smFRET.Biophysical journal · 2023Article
- Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap.iScience · 2022Article
- High Resolution Fluorescence Lifetime Maps from Minimal Photon Counts.ACS photonics · 2022Article
- Extraction of rapid kinetics from smFRET measurements using integrative detectors.Cell reports. Physical science · 2021Article
- Direct Photon-by-Photon Analysis of Time-Resolved Pulsed Excitation Data using Bayesian Nonparametrics.Cell reports. Physical science · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Effective forces derived from experimental or in silico molecular dynamics time traces are critical in developing reduced and computationally efficient descriptions of otherwise complex dynamical problems. This helps motivate why it is important to develop methods to efficiently learn effective forces from time series data. A number of methods already exist to do this when data are plentiful but otherwise fail for sparse datasets or datasets where some regions of phase space are undersampled. In addition, any method developed to learn effective forces from time series data should be minimally a priori committal as to the shape of the effective force profile, exploit every data point without reducing data quality through any form of binning or pre-processing, and provide full credible intervals (error bars) about the prediction for the entirety of the effective force curve. Here, we propose a generalization of the Gaussian process, a key tool in Bayesian nonparametric inference and machine learning, which meets all of the above criteria in learning effective forces for the first time.
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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.