AIMC Topic: RNA

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DeepCLIP: predicting the effect of mutations on protein-RNA binding with deep learning.

Nucleic acids research Jul 27, 2020
Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling...
Mice Computational Biology RNA-Binding Proteins Animals Deep Learning Protein Binding Nucleic Acid Conformation Binding Sites Nucleotide Motifs Mutation RNA Base Sequence Humans Computer Simulation
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Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

Bioinformatics (Oxford, England) Jul 1, 2020
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting...
Software Protein Binding RNA Neural Networks, Computer Protein Structure, Secondary
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iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Briefings in bioinformatics May 21, 2020
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficie...
Machine Learning Algorithms RNA Proteins Sequence Analysis DNA Internet
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Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research Mar 18, 2020
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...
Protein Isoforms Binding Sites Homeodomain Proteins High-Throughput Nucleotide Sequencing Embryo, Mammalian Organ Specificity Myeloid Ecotropic Viral Integration Site 1 Protein Gene Expression Regulation, Developmental Chromatin Immunoprecipitation Branchial Region Protein Binding Mice Computational Biology Models, Genetic Poisson Distribution RNA Animals Deep Learning
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Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques.

Combinatorial chemistry & high throughput screening Jan 1, 2020
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...
Computational Biology RNA Methylation Machine Learning
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Deep Learning in the Study of Protein-Related Interactions.

Protein and peptide letters Jan 1, 2020
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...
RNA DNA Pharmaceutical Preparations Humans Drug Design Deep Learning Protein Binding Proteins Databases, Protein
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iRNAD: a computational tool for identifying D modification sites in RNA sequence.

Bioinformatics (Oxford, England) Dec 1, 2019
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...
Reproducibility of Results Computational Biology Support Vector Machine Nucleotides RNA Base Sequence
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Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

Bioinformatics (Oxford, England) Jul 15, 2019
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...
RNA Base Sequence Sensitivity and Specificity Deep Learning DNA Neural Networks, Computer
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DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Mathematical biosciences and engineering : MBE Jul 4, 2019
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...
Pseudouridine Deep Learning Mice Computational Biology Humans RNA Machine Learning Adenosine Algorithms Saccharomyces cerevisiae Animals 5-Methylcytosine Species Specificity RNA Processing, Post-Transcriptional
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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 May 21, 2019
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...
Sequence Analysis, RNA Algorithms Humans RNA Adenocarcinoma Machine Learning Female Survival Analysis Colonic Neoplasms Male
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