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RNA

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HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

Molecular cell
RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains ...

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning.

Nature biotechnology
Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes i...

GR-m6A: Prediction of N6-methyladenosine sites in mammals with molecular graph and residual network.

Computers in biology and medicine
RNA N6-methyladenine (m6A), which is produced by the methylation of the N6 position of eukaryotic adenine, is a relatively common post-transcriptional modification on the surface of the molecule, which frequently plays a crucial role in biological pr...

DeepASDPred: a CNN-LSTM-based deep learning method for Autism spectrum disorders risk RNA identification.

BMC bioinformatics
BACKGROUND: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and others, behavioral difficulties, and a brain that processes information differently than normal. Geneti...

CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion.

Interdisciplinary sciences, computational life sciences
Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful le...

Robotic-navigated assistance in spine surgery.

The bone & joint journal
The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperative factors, radiation exposure, and complication rates in adult patients with degenerative disorders of the thoracic and lumbar spines who have underg...

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin...

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Scientific reports
Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of in...

m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.

BMC biology
BACKGROUND: RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C position of uridine catalyzed by pyrimidine methylation transferase, which is related to the development of human diseases. Accurate identification of m5U modifi...

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.

PLoS computational biology
Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raise...