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Related Concept Videos

siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
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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.
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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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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
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Related Experiment Video

Updated: Jul 20, 2025

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools
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Machine learning for small interfering RNAs: a concise review of recent developments.

Minhyeok Lee1

  • 1School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Republic of Korea.

Frontiers in Genetics
|July 31, 2023
PubMed
Summary

Machine learning is revolutionizing small interfering RNA (siRNA) research, enhancing RNA interference (RNAi) applications. This review synthesizes recent advancements (2019-2023) in AI-driven siRNA design and function.

Keywords:
SiRNA interferenceartificial intelligenceartificial neural networkbioinformaticsdeep learningmachine learningsmall interfering RNA

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Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Artificial Intelligence

Background:

  • RNA interference (RNAi) is a crucial gene-silencing mechanism.
  • Small interfering RNA (siRNA) are key effectors in RNAi pathways.
  • Traditional siRNA design faces challenges in efficacy and specificity.

Purpose of the Study:

  • To review the integration of machine learning (ML) in siRNA research from 2019-2023.
  • To highlight state-of-the-art ML methods applied to siRNA design and function.
  • To provide a comprehensive overview of ML's impact on RNAi.

Main Methods:

  • Systematic literature review of ML applications in siRNA.
  • Analysis of deep learning models for siRNA target prediction and design.
  • Synthesis of studies focusing on ML-enhanced siRNA efficacy and off-target effects.

Main Results:

  • Significant advancements in ML algorithms for predicting effective siRNA sequences.
  • Improved accuracy in identifying siRNA with minimal off-target effects.
  • Emergence of deep learning as a powerful tool in optimizing siRNA therapeutics.

Conclusions:

  • Machine learning is transforming siRNA research, accelerating RNAi applications.
  • Continued integration of AI is essential for future breakthroughs in siRNA-based therapies.
  • This review provides a critical perspective on current ML applications in siRNA.