AIMC Topic: RNA

Clear Filters Showing 321 to 330 of 335 articles

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

Bioinformatics (Oxford, England) May 15, 2019
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 ...
Algorithms Computational Biology RNA Neural Networks, Computer
View on PubMed DOI

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

Nucleic acids research Apr 23, 2019
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...
RNA High-Throughput Nucleotide Sequencing Humans Methylation Machine Learning Epigenesis, Genetic Transcriptome Protein Interaction Maps Internet Adenosine Sequence Analysis, RNA
View on PubMed DOI

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

Protein and peptide letters Jan 1, 2019
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...
Machine Learning Algorithms Computational Biology RNA Hydrophobic and Hydrophilic Interactions Nucleic Acid Conformation RNA-Binding Proteins Molecular Dynamics Simulation
View on PubMed DOI

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

Molekuliarnaia biologiia Jan 1, 2019
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...
RNA Neural Networks, Computer Nucleic Acid Conformation Molecular Dynamics Simulation
View on PubMed DOI

A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.

Bioinformatics (Oxford, England) Nov 1, 2018
MOTIVATION: Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techn...
RNA Genome Deep Learning Gene Regulatory Networks Neural Networks, Computer
View on PubMed DOI

A deep neural network approach for learning intrinsic protein-RNA binding preferences.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important...
RNA-Binding Proteins RNA Software Deep Learning Neural Networks, Computer
View on PubMed DOI

Deep-2'-O-Me: Predicting 2'-O-methylation sites by Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
2'-O-methylation (2'-O-me) of ribose moiety is one of the significant and ubiquitous post-transcriptional RNA modifications which is vital for metabolism and functions of RNA. Although recent development of new technology (Nmseq) enabled biologists t...
Methylation Algorithms RNA Neural Networks, Computer
View on PubMed DOI

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

Bioinformatics (Oxford, England) May 1, 2018
MOTIVATION: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally r...
Machine Learning RNA Proteins Binding Sites Databases, Protein Protein Binding Models, Molecular Computational Biology
View on PubMed DOI

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the a...
DNA RNA Regulatory Sequences, Nucleic Acid K562 Cells Hep G2 Cells Proteins Models, Genetic Sequence Analysis, DNA Humans Protein Binding Neural Networks, Computer Machine Learning Genomics Software Sequence Analysis, RNA
View on PubMed DOI

miRandola 2017: a curated knowledge base of non-invasive biomarkers.

Nucleic acids research Jan 4, 2018
miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows ...
RNA, Circular Cell-Free Nucleic Acids MicroRNAs Data Curation RNA, Untranslated User-Computer Interface Knowledge Bases Humans RNA, Long Noncoding Databases, Genetic RNA Biomarkers
View on PubMed DOI
Popular Topics
Recent Journals