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RNA Processing, Post-Transcriptional

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Detecting N-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.

Scientific reports 28079126
As one of the most abundant RNA post-transcriptional modifications, N-methyladenosine (mA) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. Howe...
Adenosine Computational Biology RNA RNA Processing, Post-Transcriptional Saccharomyces cerevisiae Support Vector Machine Transcriptome
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SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology 30533006
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...
Algorithms Amino Acid Sequence Base Sequence Computational Biology Databases, Nucleic Acid Databases, Protein Humans Machine Learning Protein Binding RNA Processing, Post-Transcriptional RNA-Binding Proteins RNA, Long Noncoding
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Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

Nature genetics 31133750
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...
Algorithms Alleles Autism Spectrum Disorder Computational Biology Deep Learning Gene Expression Gene Expression Regulation Genes, Reporter Genetic Association Studies Genetic Predisposition to Disease Genome, Human Genomics Humans Mutation Phenotype RNA Processing, Post-Transcriptional RNA, Untranslated Transcription, Genetic
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DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.

Mathematical biosciences and engineering : MBE 31698559
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell t...
5-Methylcytosine Adenosine Algorithms Animals Computational Biology Deep Learning Humans Machine Learning Mice Pseudouridine RNA RNA Processing, Post-Transcriptional Saccharomyces cerevisiae Species Specificity
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iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes 32397453
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...
Adenosine Algorithms Computational Biology Datasets as Topic Deep Learning Genome, Fungal Methylation RNA Processing, Post-Transcriptional RNA, Fungal ROC Curve Saccharomyces cerevisiae
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