Evidence map›Paper›PMID 36714862›Full record

ArticlePNAS nexus2022

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability.

Nikolaos Evangelou, Noah J Wichrowski, George A Kevrekidis, Felix Dietrich, Mahdi Kooshkbaghi, Sarah McFann, Ioannis G Kevrekidis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

7 authors.

Nikolaos EvangelouDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
Noah J WichrowskiDepartment of Applied Mathematics and Statistics, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.
George A KevrekidisDepartment of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, MA 01003, USA.
Felix DietrichDepartment of Informatics, Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany.ORCID https://orcid.org/0000-0002-2906-1769
Mahdi KooshkbaghiThe Program in Applied and Computational Mathematic, Princeton University, Washington Road, Princeton, NJ 08544, USA.ORCID https://orcid.org/0000-0002-6344-7382
Sarah McFannDepartment of Chemical and Biological Engineering, Princeton University, 50-70 Olden St, Princeton, NJ 08544, USA.ORCID https://orcid.org/0000-0002-9390-9349
Ioannis G KevrekidisDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of

Indexed as

data miningmanifold learningmodel order reductionparameter nonidentifiability

Identifiers

PMID36714862
PMCPMC9802152

What OpenQuestion holds

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Registered trials

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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.