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Experimental RNAi02:15

Experimental RNAi

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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RNA Interference01:23

RNA Interference

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
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Types of RNA01:23

Types of RNA

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Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
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Types of RNA01:20

Types of RNA

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Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in regulating gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA Performs Diverse...
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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Video Experimental Relacionado

Updated: Feb 28, 2026

RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Predicción de la función de ARN no codificante mediante inteligencia artificial

David da Costa Correia1, Francisco M Couto2, Hugo Martiniano3

  • 1Departamento de Promoção da Saúde e Prevenção de Doenças não Transmissiveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, Lisboa, 1649-016, Portugal; BioISI - Biosystems and Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal; LASIGE, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, Lisboa, 1749-016, Portugal.

Journal of biomedical informatics
|February 25, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio desarrolló un método para extraer relaciones de ARN no codificante (ncRNA) y fenotipo de la literatura científica utilizando Procesamiento del Lenguaje Natural (PNL) y Modelos de Lenguaje Grandes (LLM). El enfoque logró una alta puntuación F1, prometedor para la investigación futura de ncRNA.

Palabras clave:
Supervisión DistanteModelos de Lenguaje GrandesARN no codificantesExtracción de RelacionesMinería de textos

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Área de la Ciencia:

  • Bioinformática
  • Biología Computacional
  • Genómica

Sus antecedentes:

  • Los ARN no codificantes (ncRNA) desempeñan funciones cruciales en los procesos biológicos y las enfermedades.
  • La información sobre las relaciones ncRNA-fenotipo está fragmentada en la literatura científica.
  • Se necesitan métodos eficientes para agregar y normalizar estos datos dispersos.

Objetivo del estudio:

  • Desarrollar una metodología para extraer relaciones ncRNA-fenotipo de artículos científicos.
  • Combinar el Procesamiento del Lenguaje Natural (PNL) y los Modelos de Lenguaje Grandes (LLM) para esta tarea.
  • Crear un conjunto de datos de alta fidelidad y un corpus relacional para la investigación de ncRNA.

Principales métodos:

  • Se desarrolló un pipeline de PNL para agregar y normalizar datos de cinco bases de datos de ncRNA-enfermedad.
  • Se generó un corpus relacional de ncRNA-fenotipo utilizando Extracción de Relaciones por Supervisión Distante (DSRE).
  • Se aplicaron Modelos de Lenguaje Grandes (LLM) para la Extracción de Relaciones (RE), evaluando el rendimiento en un subconjunto validado del corpus.

Principales resultados:

  • Se creó un conjunto de datos de relaciones ncRNA-fenotipo de alta fidelidad con 214.300 relaciones.
  • Se generó un corpus relacional (ncoRP) con 35.295 relaciones únicas de 21.608 artículos.
  • Se logró una alta puntuación F1 de 0.978 utilizando una metodología de RE basada en LLM.

Conclusiones:

  • Se creó con éxito un conjunto de datos normalizado de ncRNA-fenotipo y un corpus relacional.
  • La metodología combinada de LLM y DSRE demuestra un alto rendimiento para la extracción automática de relaciones.
  • El conjunto de datos, el corpus y la metodología desarrollados son recursos valiosos para estudios de ncRNA y pueden aplicarse a tareas similares de extracción de relaciones biológicas.