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RNA

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Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science.

IEEE/ACM transactions on computational biology and bioinformatics
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However,...

Prediction of Multiple Types of RNA Modifications via Biological Language Model.

IEEE/ACM transactions on computational biology and bioinformatics
It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. ...

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

IEEE/ACM transactions on computational biology and bioinformatics
Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein...

WVDL: Weighted Voting Deep Learning Model for Predicting RNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-p...

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.

Nature methods
DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid struct...

RNA contact prediction by data efficient deep learning.

Communications biology
On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsit...

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...

DeepCIP: A multimodal deep learning method for the prediction of internal ribosome entry sites of circRNAs.

Computers in biology and medicine
Circular RNAs (circRNAs) have been found to have the ability to encode proteins through internal ribosome entry sites (IRESs), which are essential RNA regulatory elements for cap-independent translation. Identification of IRES elements in circRNA is ...

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning.

The CRISPR journal
Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect ma...

Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications.

Computers in biology and medicine
Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study,...