AIMC Topic: RNA, Messenger

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LGLoc as a new language model-driven graph neural network for mRNA localization.

Scientific reports
The localization of mRNA is crucial for the synthesis of functional proteins and plays a significant role in cellular processes. Understanding mRNA localization can enhance applications in disease diagnosis (e.g., cancer, Alzheimer's) and drug develo...

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.

Nucleic acids research
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a...

GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions.

BMC bioinformatics
Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation a...

A Digital Score Based on Circulating-Tumor-Cells-Derived mRNA Quantification and Machine Learning for Early Colorectal Cancer Detection.

ACS nano
Circulating tumor cells (CTCs) serve as valuable biomarkers in tumor circulation, carrying essential primary tumor information. The purification of CTCs from peripheral blood samples and the analysis of their characteristic molecules enable the detec...

Lipid nanoparticle (LNP) mediated mRNA delivery in neurodegenerative diseases.

Journal of controlled release : official journal of the Controlled Release Society
Neurodegenerative diseases (NDD) are characterized by the progressive loss of neurons and the impairment of cellular functions. Messenger RNA (mRNA) has emerged as a promising therapy for treating NDD, as it can encode missing or dysfunctional protei...

Predicting adenine base editing efficiencies in different cellular contexts by deep learning.

Genome biology
BACKGROUND: Adenine base editors (ABEs) enable the conversion of A•T to G•C base pairs. Since the sequence of the target locus influences base editing efficiency, efforts have been made to develop computational models that can predict base editing ou...

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Nucleic acids research
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human tran...

The Advances in Deep Learning Modeling of Polyadenylation Codes.

Wiley interdisciplinary reviews. RNA
3'-end cleavage and polyadenylation is an essential step of eukaryotic mRNA and lncRNA expression. The formation of a polyadenylation (polyA) site is determined by combinatory effects of multiple tandem motifs (~6 motifs in humans), each of which is ...

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning.

PloS one
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for...

AI techniques have facilitated the understanding of epitranscriptome distribution.

Cell genomics
N-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al. designed a novel Transformer-BiGRU framew...