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RNA

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iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

Journal of theoretical biology Dec 24, 2018
The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, ...
Humans Neural Networks, Computer Computational Biology RNA Methylation Machine Learning Binding Sites Algorithms Deep Learning
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RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks.

PLoS computational biology Nov 27, 2018
Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA st...
Monte Carlo Method Neural Networks, Computer Datasets as Topic Molecular Dynamics Simulation Hot Temperature RNA Nucleic Acid Conformation
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Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

Journal of theoretical biology Oct 12, 2018
RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a...
Support Vector Machine RNA Datasets as Topic Neural Networks, Computer Protein Binding Computational Biology Sequence Analysis
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Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

IEEE/ACM transactions on computational biology and bioinformatics Oct 5, 2018
Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable inform...
RNA Protein Interaction Mapping Caenorhabditis elegans Proteins Algorithms Neural Networks, Computer Protein Interaction Maps Machine Learning RNA-Binding Proteins
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A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data.

Computer methods and programs in biomedicine Oct 5, 2018
BACKGROUND AND OBJECTIVE: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine learning methods...
RNA Pattern Recognition, Automated False Positive Reactions Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Supervised Machine Learning Stomach Neoplasms Breast Neoplasms Male Diagnosis, Computer-Assisted Algorithms Deep Learning Female Lung Neoplasms Reproducibility of Results Humans Software
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BERMP: a cross-species classifier for predicting mA sites by integrating a deep learning algorithm and a random forest approach.

International journal of biological sciences Sep 7, 2018
N-methyladenosine (mA) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of mA sites identified from different species using high-throughput experiments provides a rich resource to construct ...
Adenosine Machine Learning Computational Biology Algorithms RNA Deep Learning Methylation
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RNA-seq analysis reveals different gene ontologies and pathways in rheumatoid arthritis and Kashin-Beck disease.

International journal of rheumatic diseases Sep 5, 2018
AIMS: To understand the pathogenesis of cartilage damage in Kashin-Beck disease (KBD) and rheumatoid arthritis (RA) which similar clinical symptoms.
Kashin-Beck Disease Humans Phenotype Female Aged Signal Transduction Databases, Genetic Proteoglycans Cartilage Arthritis, Rheumatoid RNA Male Gene Ontology Genetic Predisposition to Disease Middle Aged Inflammation Mediators Sequence Analysis, RNA Gene Regulatory Networks
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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...
Genome, Human RNA Interference Support Vector Machine RNA Methylation Software Humans Algorithms Computational Biology
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iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

Journal of theoretical biology Jul 19, 2018
N- methyladenosine (mA) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). A...
Neural Networks, Computer Adenosine Support Vector Machine Sequence Analysis, RNA RNA Base Sequence
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Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

BMC genomics Jul 3, 2018
BACKGROUND: RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence ...
User-Computer Interface Protein Binding Computational Biology Neural Networks, Computer Algorithms RNA-Binding Proteins RNA Binding Sites Nucleic Acid Conformation Nucleotide Motifs
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