AIMC Topic: RNA

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Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

BMC bioinformatics Jun 10, 2019
BACKGROUND: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discove...
Neural Networks, Computer Algorithms Animals Single-Cell Analysis Humans Cluster Analysis RNA Base Sequence Gene Ontology Mice Sequence Analysis, RNA Databases, Genetic
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Robotic assisted generation of 2'-deoxy-2'-fluoro-modifed RNA aptamers - High performance enabling strategies in aptamer selection.

Methods (San Diego, Calif.) May 30, 2019
Aptamer selection is a laborious procedure, requiring expertise and significant resources. These characteristics limit the accessibility of researchers to these molecular tools. We describe a selection procedure, making use of a robotic system that a...
Humans High-Throughput Nucleotide Sequencing Pyrimidines Aptamers, Nucleotide Robotics RNA
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Pre-Treatment Biomarkers of Anti-Tumour Necrosis Factor Therapy Response in Crohn's Disease-A Systematic Review and Gene Ontology Analysis.

Cells May 28, 2019
The most prominent treatment for the serious cases of Crohn's disease (CD) are biological tumour necrosis factor (TNF) inhibitors. Unfortunately, therapy nonresponse is still a serious issue in ~1/3 of CD patients. Accurate prediction of responsivene...
Biomarkers Polymorphism, Single Nucleotide Crohn Disease Gene Ontology Humans RNA Tumor Necrosis Factor-alpha Genome, Human
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Recent methodology progress of deep learning for RNA-protein interaction prediction.

Wiley interdisciplinary reviews. RNA May 8, 2019
Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been r...
RNA-Binding Proteins RNA Deep Learning
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MLSeq: Machine learning interface for RNA-sequencing data.

Computer methods and programs in biomedicine Apr 29, 2019
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data....
RNA Programming Languages Linear Models Support Vector Machine Poisson Distribution Discriminant Analysis Humans Machine Learning Gene Expression Profiling Sequence Analysis, RNA Software Algorithms
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RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 11, 2019
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-pr...
Neural Networks, Computer RNA-Binding Proteins RNA Binding Sites Algorithms Protein Binding Computational Biology
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Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods Mar 25, 2019
A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions...
RNA High-Throughput Nucleotide Sequencing Gene Expression Regulation Exons Alternative Splicing K562 Cells RNA Splicing Humans Hep G2 Cells Algorithms Models, Statistical Deep Learning Reproducibility of Results Bayes Theorem Sequence Analysis, RNA Gene Expression Profiling Signal Processing, Computer-Assisted Epigenomics
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Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Nature methods Mar 18, 2019
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly i...
RNA Cluster Analysis Gene Expression Profiling Brain Mapping Transcriptome Deep Learning Principal Component Analysis Computational Biology Retina Sequence Analysis, RNA Intestines Single-Cell Analysis Inflammation Computer Simulation Mice Databases, Genetic Reproducibility of Results Software Animals Algorithms Leukocytes, Mononuclear Phenotype
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EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions.

Analytical biochemistry Jan 18, 2019
RNA editing process like Adenosine to Intosine (A-to-I) often influences basic functions like splicing stability and most importantly the translation. Thus knowledge about editing sites is of great importance in molecular biology. With the growth of ...
User-Computer Interface ROC Curve Area Under Curve RNA Machine Learning Adenosine Inosine RNA Editing
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DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning.

BMC bioinformatics Dec 31, 2018
BACKGROUND: N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an ...
Alternative Splicing Mice Deep Learning Humans Computational Biology Methylation Embryonic Stem Cells Zebrafish Liver Neoplasms Brain Adenosine RNA Sequence Analysis, RNA Carcinoma, Hepatocellular Animals
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