AIMC Topic: RNA-Binding Proteins

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Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

Protein and peptide letters Jan 1, 2020
In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well ...
RNA, Long Noncoding Proteins Protein Binding RNA-Binding Proteins Supervised Machine Learning Databases, Nucleic Acid Databases, Protein Models, Molecular Software Computer Simulation Humans
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Biogenesis mechanisms of circular RNA can be categorized through feature extraction of a machine learning model.

Bioinformatics (Oxford, England) Dec 1, 2019
MOTIVATION: In recent years, multiple circular RNAs (circRNA) biogenesis mechanisms have been discovered. Although each reported mechanism has been experimentally verified in different circRNAs, no single biogenesis mechanism has been proposed that c...
Humans Machine Learning RNA, Circular RNA Splicing RNA-Binding Proteins RNA Splice Sites
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Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data.

Bioinformatics (Oxford, England) Sep 15, 2019
MOTIVATION: Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distin...
RNA-Binding Proteins Prostatic Neoplasms Machine Learning Male Caveolae Caveolin 1 Humans
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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...
RNA Hydrophobic and Hydrophilic Interactions Nucleic Acid Conformation RNA-Binding Proteins Molecular Dynamics Simulation Computational Biology Machine Learning Algorithms
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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
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A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research Aug 21, 2017
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...
Base Sequence Binding Sites Nucleotide Motifs Binding, Competitive High-Throughput Nucleotide Sequencing Gene Expression Regulation Internet Algorithms 3' Untranslated Regions Reproducibility of Results Humans RNA-Binding Proteins Machine Learning Protein Binding Mutation Computational Biology
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RBPPred: predicting RNA-binding proteins from sequence using SVM.

Bioinformatics (Oxford, England) Mar 15, 2017
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...
RNA-Binding Proteins RNA Software Humans Support Vector Machine Computational Biology
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