Evidence map›Paper›PMID 34485995›Full record

ArticleControl Conference (ECC) ... European. European Control Conference2020

Estimation of general time-varying single particle tracking linear models using local likelihood.

Boris I Godoy, Nicholas A Vickers, Y Lin, Sean B Andersson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Control Conference (ECC) ... European. European Control Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. A time-varying approach to single particle tracking with a nonlinear observation model.Proceedings of the ... American Control Conference. American Control Conference · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Boris I GodoyDepartment of Mechanical Engineering, Boston University, Boston, MA, 02215 USA.
Nicholas A VickersDepartment of Mechanical Engineering, Boston University, Boston, MA, 02215 USA.
Y LinDivision of Systems Engineering, Boston University, Boston, MA, 02215 USA.
Sean B AnderssonDepartment of Mechanical Engineering, Boston University, Boston, MA, 02215 USA.

Funding

Joint estimation of motion model, model parameters, and particle trajectories in single particle trackingR01GM117039 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI ANDERSSON, SEAN B. · 2017 to 2021
$1.7M
NIGMS NIH HHS R01 GM117039
6 · The paper itself

Abstract

In this work, we study a general approach to the estimation of single particle tracking models with time-varying parameters. The main idea is to use local Maximum Likelihood (ML), applying a sliding window over the data and estimating the model parameters in each window. We combine local ML with Expectation Maximization to iteratively find the ML estimate in each window, an approach that is amenable to generalization to nonlinear models. Results using controlled-experimental data generated in our lab show that our proposed algorithm is able to track changes in the parameters as they evolve during a trajectory under real-world experimental conditions, outperforming other algorithms of similar nature.

Identifiers

PMID34485995
PMCPMC8411989

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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.