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Videos de Conceptos Relacionados

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
400
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.

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Updated: Jan 8, 2026

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GaugeFixer: superación de la no identificabilidad de parámetros en modelos de relaciones secuencia-función

Carlos Martí-Gómez1, David M McCandlish1, Justin B Kinney1

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, 1 Bungtown Rd., Cold Spring Harbor, 11724, New York, United States.

bioRxiv : the preprint server for biology
|December 22, 2025
PubMed
Resumen

Los modelos de biología computacional tienen parámetros ambiguos ("libertades de calibre") que dificultan la interpretación. GaugeFixer, un nuevo paquete de Python, resuelve estas ambigüedades con escalado lineal, permitiendo el análisis de grandes paisajes de secuencia-función.

Sus antecedentes:

  • Los modelos de relación secuencia-función son cruciales en biología computacional.
Palabras clave:
biología computacionalbioinformáticamodelado matemáticorelaciones secuencia-funciónlibertades de calibreambigüedad de parámetrosescalado linealpaisajes de secuencia-funcióninterpretación de modelosoptimización computacional

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  • Los parámetros del modelo a menudo tienen ambigüedades, denominadas "libertades de calibre", que impiden la interpretación directa.
  • Los métodos existentes para resolver las libertades de calibre son computacionalmente intensivos, lo que limita la escalabilidad.
  • Conclusiones:

    • GaugeFixer proporciona una solución eficiente y escalable para interpretar modelos de secuencia-función.
    • La herramienta facilita una comprensión biológica más profunda de las relaciones secuencia-función.
    • GaugeFixer aborda una necesidad crítica insatisfecha en las herramientas de biología computacional.