Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Classification of Systems-I01:26

Classification of Systems-I

742
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
742
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

1.0K
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
1.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

587
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
587
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
Survival Tree01:19

Survival Tree

499
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...
499

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Epigenetic signatures of cardiometabolic risk in men: accelerated aging and differential methylation replicated across cohorts.

Clinical epigenetics·2026
Same author

Ferroptosis with Contributions from Apoptosis and Necroptosis in Porphyrazine III-Based Photodynamic Therapy of Primary Human Gliomas.

Pharmaceutics·2026
Same author

Photo- and Immunotherapy Interface: Can Dendritic Cell Vaccines Overcome the Limitations of PDT?

Pharmaceutics·2026
Same author

Synolitic Graph Neural Networks for MRI-Derived Radiomic-Based Prediction of Prostate Cancer Progression on Active Surveillance.

Cancers·2026
Same author

Sex-Stratified Machine Learning for the Prediction of Post-COVID Condition: A Longitudinal Cohort Study.

Journal of clinical medicine·2026
Same author

From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.

International journal of molecular sciences·2026

Video Experimental Relacionado

Updated: May 1, 2026

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs
08:35

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs

Published on: July 20, 2017

13.0K

Modelos de aprendizaje automático explicables para la clasificación de subtipos de glioma y la predicción de

Olga Vershinina1,2, Victoria Turubanova1,2,3, Mikhail Krivonosov1,2

  • 1Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.

Cancers
|August 28, 2025
PubMed
Resumen

Los modelos de aprendizaje automático explicables clasifican con precisión los subtipos de glioma y predicen la supervivencia del paciente utilizando datos de secuencia de ARN. Los genes clave identificados ofrecen información sobre la biología del tumor y el pronóstico para mejorar la toma de decisiones clínicas.

Palabras clave:
Inteligencia artificial explicabledatos de expresión génicael gliomaAprendizaje automáticopronóstico de supervivencia globalClasificación de los subtipos

Más Videos Relacionados

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

8.0K
Translational Orthotopic Models of Glioblastoma Multiforme
07:37

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

3.0K

Videos de Experimentos Relacionados

Last Updated: May 1, 2026

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs
08:35

A Human Glioblastoma Organotypic Slice Culture Model for Study of Tumor Cell Migration and Patient-specific Effects of Anti-Invasive Drugs

Published on: July 20, 2017

13.0K
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

8.0K
Translational Orthotopic Models of Glioblastoma Multiforme
07:37

Translational Orthotopic Models of Glioblastoma Multiforme

Published on: February 17, 2023

3.0K

Área de la Ciencia:

  • En el campo de la oncología
  • La bioinformática
  • Biología computacional

Sus antecedentes:

  • Los gliomas son tumores cerebrales agresivos con mal pronóstico, que requieren un diagnóstico temprano y preciso.
  • La clasificación del tumor y la predicción de la supervivencia son críticas para las estrategias efectivas de tratamiento del glioma.

Objetivo del estudio:

  • Desarrollar y validar modelos de aprendizaje automático explicables para la clasificación de los subtipos de glioma (astrocitoma, oligodendroglioma, glioblastoma).
  • Para predecir las tasas de supervivencia de los pacientes utilizando datos de secuenciación de ARN (RNA-seq).
  • Mejorar la transparencia de los modelos mediante el análisis de las explicaciones aditivas de Shapley (SHAP).

Principales métodos:

  • Análisis de conjuntos de datos de secuencias de ARN disponibles para el público.
  • Aplicación de la selección de características para identificar los biomarcadores genéticos clave.
  • Desarrollo y comparación de varios modelos ML para la clasificación y el análisis de la supervivencia.
  • Interpretación de las predicciones del modelo utilizando los valores SHAP.

Principales resultados:

  • Se identificaron trece genes clave (por ejemplo, TERT, VEGFA, MMP9) como significativamente asociados con los subtipos de glioma y la supervivencia.
  • La Máquina Vectorial de Apoyo (SVM) logró una precisión equilibrada de 0,816 y un AUC de 0,896 para la clasificación.
  • El modelo de regresión de Cox de caso-control (CoxCC) demostró una fuerte predicción de supervivencia con un índice C de 0,809.
  • El análisis SHAP proporcionó información sobre la influencia de la expresión génica en los resultados del modelo.

Conclusiones:

  • Los modelos de ML explicables desarrollados ofrecen una herramienta sólida para el diagnóstico y el pronóstico del glioma.
  • Estos modelos pueden ayudar a los médicos a adaptar las estrategias de tratamiento para mejorar los resultados de los pacientes.
  • Los biomarcadores genéticos identificados tienen potencial para una mayor investigación sobre la patogénesis del glioma.