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RNA

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Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques.

Combinatorial chemistry & high throughput screening
BACKGROUND: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune rec...

Deep Learning in the Study of Protein-Related Interactions.

Protein and peptide letters
Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In re...

iRNAD: a computational tool for identifying D modification sites in RNA sequence.

Bioinformatics (Oxford, England)
MOTIVATION: Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased...

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neur...

DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Mathematical biosciences and engineering : MBE
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell t...

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

Briefings in bioinformatics
MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We coll...

Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles.

Bioinformatics (Oxford, England)
MOTIVATION: The de novo prediction of RNA tertiary structure remains a grand challenge. Predicted RNA solvent accessibility provides an opportunity to address this challenge. To the best of our knowledge, there is only one method (RNAsnap) available ...

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

Nucleic acids research
N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction fram...

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions.

Protein and peptide letters
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to det...

[An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-layer Neural Networks].

Molekuliarnaia biologiia
A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present app...