ArticlePloS one2025
A new method for a priori practical identifiability.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Employing Observability Rank Conditions for Taking into Account Experimental Information a priori.Bulletin of mathematical biology · 2025Article
- Mechanistic model for human brain metabolism and its connection to the neurovascular coupling.PLoS computational biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivePractical identifiability analysis, i.e., ascertaining whether a model property can be determined from given data, is central to model-based data analysis in biomedicine. The main approaches used today all require that coverage of the parameter space be exhaustive, which is usually impossible. An alternative could be using structural identifiability methods, since they do not need such coverage. However, current structural methods are unsuited for practical identifiability analysis, since they assume that all higher-order derivatives of the measured variables are available. Herein, we provide new definitions and methods that allow for this assumption to be relaxed. METHODS AND
resultsWe introduce the concept of [Formula: see text]-identifiability, which differs from previous definitions in that it assumes that only the first [Formula: see text] derivatives of the measurement signal yi are available. This new type of identifiability can be determined using our new algorithms, as is demonstrated by applications to various published biomedical models. Our methods allow for identifiability of not only parameters, but of any model property, i.e., observability. These new results provide further strengthening of conclusions made in previous analysis of these models. For the first time, we can quantify the impact of the assumption that all derivatives are available in specific examples. If one, e.g., assumes that only up to third order derivatives, instead of all derivatives, are available, the number of identifiable parameters drops from 17 to 1 for a Drosophila model, and from 21 to 6 for an NF-[Formula: see text]B model. In both models, the previously obtained identifiability is present only if at least 20 derivatives of all measurement signals are available.
conclusionOur results demonstrate that the assumption regarding availability of derivatives made in traditional structural identifiability analysis requires a big overestimation regarding the number of parameters that can be estimated. Our new methods and algorithms allow for this assumption to be relaxed, bringing structural identifiability methodology one step closer to practical identifiability analysis.
Indexed as
Identifiers
What OpenQuestion holds
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.