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

Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Machines: Problem Solving II01:30

Machines: Problem Solving II

674
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
674
Machines: Problem Solving I01:22

Machines: Problem Solving I

719
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
719
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Associative Learning01:27

Associative Learning

1.4K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.4K
Purposive Learning01:22

Purposive Learning

513
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
513

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

Bioinformatics (Oxford, England)·2026
Same author

Riemannian metric learning for alignment of spatial multiomics.

Bioinformatics (Oxford, England)·2026
Same author

Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer.

Cancer research·2026
Same author

The tree labeling polytope: A unified approach to ancestral reconstruction problems.

Cell systems·2026
Same author

Dango: Predicting higher-order genetic interactions.

Cell systems·2026
Same author

Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.

Cancer discovery·2026

Video Experimental Relacionado

Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Aprendizaje automático visible para la biomedicina

Michael K Yu1, Jianzhu Ma2, Jasmin Fisher3

  • 1Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.

Cell
|June 16, 2018
PubMed
Resumen
Este resumen es generado por máquina.

La inteligencia artificial puede mejorar las terapias analizando los datos de los pacientes. Los enfoques visibles de aprendizaje automático, guiados por la biología experimental, abordan desafíos como la variedad de datos y la falta de conocimiento en las predicciones biomédicas.

Más Videos Relacionados

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Videos de Experimentos Relacionados

Last Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.1K

Área de la Ciencia:

  • Investigación biomédica
  • Inteligencia artificial
  • Aprendizaje automático

Sus antecedentes:

  • La traducción de los datos de los pacientes en terapias efectivas es un objetivo clave para la inteligencia artificial (IA).
  • Los modelos de aprendizaje automático (ML) en biomedicina luchan con datos muy diversos y comprenden la base biológica de sus predicciones.
  • La falta de conocimiento mecanicista dificulta la aplicación clínica de la IA en la medicina.

Objetivo del estudio:

  • Abogar por enfoques de inteligencia artificial "visibles" en la biomedicina.
  • Proponer la integración de los principios de la biología experimental en el diseño de modelos de aprendizaje automático.
  • Mejorar la interpretabilidad y confiabilidad de las estrategias terapéuticas impulsadas por la IA.

Principales métodos:

  • Desarrollo de marcos de aprendizaje automático que incorporen conocimiento biológico.
  • Diseño de modelos de IA con estructuras informadas por la biología experimental.
  • Centrarse en enfoques "visibles" para una mayor transparencia en las predicciones.

Principales resultados:

  • Los métodos de IA visible propuestos ofrecen un camino para superar los desafíos de la heterogeneidad de los datos.
  • La integración de la biología experimental mejora la comprensión mecanicista de las predicciones de IA.
  • Los enfoques visibles mejoran la traducción de los datos del paciente a las terapias.

Conclusiones:

  • La IA visible, guiada por la biología experimental, es crucial para el avance de las aplicaciones biomédicas.
  • Este enfoque aborda las limitaciones clave del aprendizaje automático actual en medicina.
  • Facilita un desarrollo terapéutico impulsado por IA más confiable e interpretable.