AIMC Topic: RNA Interference
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Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d.
BioTechniques
Feb 29, 2024
In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA knockdown and Cas9-mediated DNA targeting in zebrafish embryos. They targeted the no tail () gene as...
Biological features between miRNAs and their targets are unveiled from deep learning models.
Scientific reports
Dec 10, 2021
MicroRNAs (miRNAs) are ~ 22 nucleotide ubiquitous gene regulators. They modulate a broad range of essential cellular processes linked to human health and diseases. Consequently, identifying miRNA targets and understanding how they function are critic...
Identification of synthetic lethality based on a functional network by using machine learning algorithms.
Journal of cellular biochemistry
Aug 20, 2018
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 ge...
iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens.
Journal of computational biology : a journal of computational molecular cell biology
Aug 16, 2018
2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methy...
Digging deep into Golgi phenotypic diversity with unsupervised machine learning.
Molecular biology of the cell
Oct 11, 2017
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportuni...
Using machine learning algorithms to identify genes essential for cell survival.
BMC bioinformatics
Oct 3, 2017
BACKGROUND: With the explosion of data comes a proportional opportunity to identify novel knowledge with the potential for application in targeted therapies. In spite of this huge amounts of data, the solutions to treating complex disease is elusive....
Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework.
Briefings in functional genomics
Dec 6, 2024
RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in differ...
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.
Briefings in bioinformatics
Sep 23, 2024
The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequ...
A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.
Briefings in bioinformatics
Jul 25, 2024
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Her...
COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants.
Briefings in functional genomics
May 18, 2023
Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery can target viral RNAs for destruction because virus-derived small interfering RNAs (vsiRNAs) are fou...
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