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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data07:11

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We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.
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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Relative Motion Analysis - Velocity01:24

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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Here, we present a protocol to explore the biomarker and survival predictor of breast cancer based on the comprehensive analysis of pooled clinical datasets derived from a variety of publicly accessible databases, using the strategy of expression, correlation and survival analysis step by...
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy12:09

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The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI...
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Video Experimental Relacionado

Updated: Jan 20, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

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Análisis de datos de la red relacional multiplicativa reconciliada de dos etapas

M Burak Erturan1

  • 1Dr.,Head Supply Engineer, General Directorate of State Hydraulic Works, Directorate of Region 13, Barış Mah. Halide Edip Cad. Kepez Antalya, Turkiye.

MethodsX
|January 19, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta el modelo Reconciled Multiplicative Relational (RMR) para sistemas de dos etapas. RMR ofrece una solución única de evaluación de la eficiencia, superando las limitaciones de los métodos anteriores de análisis envolvente de datos.

Palabras clave:
análisis envolvente de datosreconciliación de datoseficienciamultiplicativorelacionalDEA de red de dos etapas

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Área de la Ciencia:

  • Investigación de Operaciones
  • Ciencia de la Gestión
  • Ingeniería Industrial

Sus antecedentes:

  • Los sistemas de red de dos etapas requieren métodos especializados de evaluación de la eficiencia.
  • Los modelos multiplicativos relacionales tradicionales en el Análisis Envolvente de Datos (DEA) pueden sufrir de soluciones de eficiencia no únicas.
  • Los modelos existentes a menudo priorizan subprocesos, lo que puede no reflejar el rendimiento real del sistema.

Objetivo del estudio:

  • Presentar una metodología novedosa para una evaluación de la eficiencia más justa en sistemas de dos etapas.
  • Abordar el problema de no unicidad en los modelos multiplicativos relacionales de DEA.
  • Introducir un modelo que no prioriza ningún subproceso.

Principales métodos:

  • Desarrollo del modelo Reconciled Multiplicative Relational (RMR).
  • Aplicación de técnicas de reconciliación de datos dentro de un marco relacional de DEA.
  • Determinación simultánea de los valores máximos de eficiencia para todos los procesos bajo restricciones relacionales.

Principales resultados:

  • El modelo RMR proporciona una solución única de evaluación de la eficiencia.
  • La metodología supera el problema de no unicidad inherente a algunos modelos relacionales de DEA.
  • El modelo RMR permite la evaluación simultánea de la eficiencia sin priorización de procesos.

Conclusiones:

  • El modelo RMR ofrece una evaluación de la eficiencia más justa y robusta para sistemas de dos etapas.
  • Este enfoque es particularmente útil cuando no se prefieren Unidades de Toma de Decisiones (DMU) o no hay información previa disponible.
  • El modelo RMR mejora la aplicabilidad del DEA relacional en entornos operativos complejos.