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RNA

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

Bioinformatics (Oxford, England)
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...

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

Bioinformatics (Oxford, England)
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...

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
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...

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

Bioinformatics (Oxford, England)
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...

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

Bioinformatics (Oxford, England)
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...

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

Nucleic acids research
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 ...

Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

Nucleic acids research
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...

RBPPred: predicting RNA-binding proteins from sequence using SVM.

Bioinformatics (Oxford, England)
MOTIVATION: Detection of RNA-binding proteins (RBPs) is essential since the RNA-binding proteins play critical roles in post-transcriptional regulation and have diverse roles in various biological processes. Moreover, identifying RBPs by computationa...

Primer on the Gene Ontology.

Methods in molecular biology (Clifton, N.J.)
The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essent...

The Gene Ontology and the Meaning of Biological Function.

Methods in molecular biology (Clifton, N.J.)
The Gene Ontology (GO) provides a framework and set of concepts for describing the functions of gene products from all organisms. It is specifically designed for supporting the computational representation of biological systems. A GO annotation is an...