Study on aptamer based high throughput approach identifies natural ingredients against <scp>RGNNV</scp>

Wei, Hongling; Liu, Mingzhu; Ke, Ke; Xiao, Shuangyan; Huang, Lin; He, Qiongyu; Mo, Changping; Pang, Hai; Xiao, Guozhu; Li, Pengfei; Yu, Qing

doi:10.1111/jfd.13693

Cited by 2 publications

(3 citation statements)

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“…According to the findings of a study carried out by [21], the Singapore grouper iridovirus (SGIV) is responsible for causing significant economic losses in mariculture. There is a critical and immediate need for efficient therapies for SGIV infection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…AHTS should be suitable for the speedy identification of components that are efficient against other viruses, according to [21]. [21] Non-coding RNAs (ncRNAs), which account for the majority of the genome, perform a variety of complex and specific activities, and it is essential to understand these roles to get an understanding of almost every aspect of cancer. This extensive group of chemicals is responsible for important activities in the control mechanisms of a variety of cellular processes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The reason behind selecting the 25-mer RNA sequence is that; the SELEX process selects the aptamers of size 30-mer to 60-mer from the oligonucleotide libraries [43][44][45][46]. Though, aptamers are normally smaller than the size of 30-mer [21][22]. Therefore, the main focus of this study was to produce 25-mer RNA aptamers to target the protein.…”

Section: Proposed Mlpmentioning

confidence: 99%

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Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Mahmood¹,

Malik²,

Khan³

et al. 2022

JCBI

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Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%