Search research articles

ACERCA DE JoVE

Visión General Liderazgo Blog Centro de Ayuda JoVE

AUTORES

Proceso de Publicación Consejo Editorial Alcance y Políticas Revisión por Pares Preguntas Frecuentes Enviar

BIBLIOTECARIOS

Testimonios Suscripciones Acceso Recursos Consejo Asesor de Bibliotecas Preguntas Frecuentes

INVESTIGACIÓN

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archivo

EDUCACIÓN

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Centro de Recursos para Profesores Sitio de Profesores

Términos y Condiciones de Uso

Política de Privacidad

Videos de Conceptos Relacionados

DNA as a Genetic Template

DNA as a Genetic Template

Labeling DNA Probes

Labeling DNA Probes

DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

También podría leer

Artículos Relacionados

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

Ordenar por

Same author

Heat-rechargeable computation in DNA logic circuits and neural networks.

Nature·2025

Same author

Modular reconfiguration of DNA origami assemblies using tile displacement.

Science robotics·2023

Same author

DNA Strand-Displacement Temporal Logic Circuits.

Journal of the American Chemical Society·2022

Same author

Developmental Self-Assembly of a DNA Ring with Stimulus-Responsive Size and Growth Direction.

Journal of the American Chemical Society·2022

Same author

A Loser-Take-All DNA Circuit.

ACS synthetic biology·2021

Same author

A Cooperative DNA Catalyst.

Journal of the American Chemical Society·2021

Same journal

Publisher Correction: In situ nanocrystal confinement for efficient blue perovskite LEDs.

Nature·2026

Same journal

Ligand-enabled distal desaturative lactonization of aliphatic acids.

Nature·2026

Same journal

Base editing reveals an essential role for NANOG in human embryogenesis.

Nature·2026

Same journal

US funding uncertainties threaten to sink key global oceanography projects.

Nature·2026

Same journal

Electric fields probe the symmetry of the 'heavy hydrogen' nucleus.

Nature·2026

Same journal

'Edited' human embryos reveal secrets of our development - and fuel ethical debate.

Nature·2026

Ver todos los artículos relacionados

Search research articles

Home
Aprendizaje Supervisado En Redes Neuronales De Adn

Home
Aprendizaje Supervisado En Redes Neuronales De Adn

Video Experimental Relacionado

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Aprendizaje supervisado en redes neuronales de ADN

Kevin M Cherry¹, Lulu Qian^2,3,4

¹Bioengineering, California Institute of Technology, Pasadena, CA, USA.

|September 3, 2025

Ver abstracta en PubMed

Resumen

Este resumen es generado por máquina.

Este estudio demuestra que las moléculas de ADN realizan de forma autónoma el aprendizaje supervisado in vitro. Estos sistemas moleculares aprenden la clasificación de patrones, allanando el camino para máquinas con capacidades de aprendizaje integradas.

Más Videos Relacionados

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Videos de Experimentos Relacionados

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Área de la Ciencia:

Ingeniería de sistemas moleculares
Inteligencia artificial
La biofísica

Sus antecedentes:

El aprendizaje biológico conduce a comportamientos complejos.
Los principios de computación neuronal sustentan el aprendizaje automático.
Los sistemas moleculares sintéticos han mostrado potencial para la computación neuronal.

Objetivo del estudio:

Para permitir que los sistemas no vivos con capacidades de aprendizaje.
Investigar el aprendizaje autónomo en sistemas moleculares sintéticos.
Para programar moléculas de ADN para el aprendizaje supervisado in vitro.

Principales métodos:

Desarrolló una red neuronal de ADN.
Datos de entrenamiento integrados en memorias de concentración molecular.

Se utilizaron memorias moleculares para procesar los datos de prueba para la clasificación de patrones.

Principales resultados:

Aprendizaje supervisado autónomo demostrado in vitro utilizando ADN.
Entrenó con éxito una red neuronal de ADN para clasificar patrones de 100 bits.
Mostró los sistemas moleculares que aprenden tareas complejas de clasificación de patrones.

Conclusiones:

Los circuitos moleculares pueden aprender tareas más allá de los comportamientos adaptativos simples.
Permite el desarrollo de máquinas moleculares con aprendizaje y toma de decisiones integrados.
Abre posibilidades para aplicaciones en biomedicina y materiales blandos.