24 papers · ranked by Valyu relevance
Martina Conte, Ryan T. Woodall, Margarita Gutova, Bihong T. Chen + 4 more
Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of…
Yuganthi R. Liyanage, Gerardo Chowell, Gleb Pogudin, Necibe Tuncer
Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we…
Marisa C. Eisenberg, Michael A. L. Hayashi
Identifiability is a necessary condition for successful parameter estimation of dynamic system models. A major component of identifiability analysis is determining the identifiable parameter combinations, the functional forms for the dependencies between unidentifiable parameters. Identifiable combinations can help in…
David L. I. Janzén, Linnéa Bergenholm, Mats Jirstrand, Joanna Parkinson + 3 more
'Joanna Parkinson' 'James Yates' 'Neil D. Evans' 'Michael J. Chappell'] Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output…
Maxime Méloux, Giada Dirupo, François Portet, Maxime Peyrard
In a striking neuroscience study, the authors placed a dead salmon in an MRI scanner and showed it images of humans in social situations. Astonishingly, standard analyses of the time reported brain regions predictive of social emotions. The explanation, of course, was not supernatural cognition but a cautionary tale…
Che Cheng, Hau-Hung Yang, Yung-Fong Hsu
The family of polychoric models (PM) categories ordinal data with latent multivariate normal variables. This modeling framework is commonly used to study the association between ordinal variables, often leading to a polychoric correlation model (PCM). Moreover, PM subsumes several well-known psychometric models, such…
Clément Yvernes, Emilie Devijver, Marianne Clausel, Eric Gaussier
Identifying the effect of a treatment from observational data typically requires assuming a fully specified causal diagram. However, such diagrams are rarely known in practice, especially in complex or high-dimensional settings. To overcome this limitation, recent works have explored the use of causal…
Peter Thompson, Benjamin Jan Andersson, Gunnar Cedersund
Practical identifiability analysis – to determine 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 the coverage of the parameter space be exhaustive, which is usually not possible. An attractive…
Yuganthi R. Liyanage, Omar Saucedo, Necibe Tuncer, Gerardo Chowell
Structural identifiability is the theoretical ability to uniquely recover model parameters from ideal, noise-free data and is a prerequisite for reliable parameter estimation in epidemic modeling. Despite its relevance in model calibration and inference, structural identifiability analysis remains underused and…
Oana-Teodora Chis, Julio R. Banga, Eva Balsa-Canto, Johannes Jaeger
Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be…
Guillaume Basse, Iavor Bojinov
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective. This definition raises important questions: Are…
Mario Castro, Rob J. de Boer
Successful mathematical modeling of biological processes relies on the expertise of the modeler to capture the essential mechanisms in the process at hand and on the ability to extract useful information from empirical data. The very structure of the model limits the ability to infer numerical values for the…
Yuganthi R. Liyanage, Nora Heitzman-Breen, Necibe Tuncer, Stanca M. Ciupe
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model’s ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for…
Christian Hennig
It is shown that some theoretically identifiable parameters cannot be empirically identified, meaning that no consistent estimator of them can exist. An important example is a constant correlation between Gaussian observations (in presence of such correlation not even the mean can be empirically identified). Empirical…
Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi
Machine learning algorithms can produce biased outcome/prediction, typically, against minorities and under-represented sub-populations. Therefore, fairness is emerging as an important requirement for the large scale application of machine learning based technologies. The most commonly used fairness notions (e.g.…
Stefano Noventa, Sangbeak Ye, Augustin Kelava, Andrea Spoto
The present work aims at showing that the identification problems (here meant as both issues of empirical indistinguishability and unidentifiability) of some item response theory models are related to the notion of identifiability in knowledge space theory. Specifically, that the identification problems of the 3- and…
Victoria Wang, John V. Tucker
Surveillance is a social phenomenon that is general and commonplace, employed by governments, companies and communities. Its ubiquity is due to technologies for gathering and processing data; its strong and obvious effects raise difficult social questions. We give a general definition of surveillance that captures the…
Arsalan Rahimabadi, Habib Benali
Parameterization and a priori identifiability analysis are two interconnected steps that should be carried out in advance of model calibration. In the first place, we propose a framework for parameterizing a recently introduced and analytically studied generalization of the celebrated…
Corey Horien, Xilin Shen, Dustin Scheinost, R. Todd Constable
Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable…
Judea Pearl
A causal claim is any assertion that invokes causal relationships between variables for ex ample that a drug has a certain eect on preventing a disease Causal claims are es tablished through a combination of data and a set of causal assumptions called a causal model A claim is robust when it is insen sitive to…
David Wörner
The identity of indiscernibles (PII) states that indiscernible objects must be identical. Many philosophers have held that the PII turns out to be either true but trivial, or non-trivial but false, depending on how the notion of (in)discernibility is spelled out. In this paper, I propose and defend an account of this…
James Swift, Matthew Arran Turner, James Christopher Reynolds
A rapid headspace analysis method for the authenticity testing of whiskies of different brands and years was developed for a low cost, deployable atmospheric pressure ionisation mass spectrometer, which required minimal sample preparation. Principal component analysis was applied to the time-averaged mass spectra, the…
Authors not listed
Machine learning holds significant promise for accelerating biomarker discovery in clinical proteomics, yet its real-world impact remains limited by widespread methodological pitfalls and unrealistic expectations. In this perspective, we critically examine the integration of machine learning into clinical proteomics…
Andrew Lee, Sarah N. Elliott, Hassan Harb, Logan Ward + 3 more
Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, we report a new method for predicting synthesizability using a simple, yet accurate thermochemical descriptor. We introduce E_min, the energy difference between a molecule and…