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Sequence Analysis, RNA

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Identification of immune-related biomarkers for intracerebral hemorrhage diagnosis based on RNA sequencing and machine learning.

Frontiers in immunology
BACKGROUND: Intracerebral hemorrhage (ICH) is a severe stroke subtype with high morbidity, disability, and mortality rates. Currently, no biomarkers for ICH are available for use in clinical practice. We aimed to explore the roles of RNAs in ICH path...

Predicting RNA sequence-structure likelihood via structure-aware deep learning.

BMC bioinformatics
BACKGROUND: The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many rese...

Wfold: A new method for predicting RNA secondary structure with deep learning.

Computers in biology and medicine
Precise estimations of RNA secondary structures have the potential to reveal the various roles that non-coding RNAs play in regulating cellular activity. However, the mainstay of traditional RNA secondary structure prediction methods relies on thermo...

Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis.

Annals of medicine
BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) presents a severe respiratory challenge with a poor prognosis due to the lack of reliable biomarkers. Recent evidence suggests that Endoplasmic Reticulum Stress (ERS) may be associated with IPF pathogen...

Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.

International journal of molecular sciences
Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the de...

A natural language processing system for the efficient extraction of cell markers.

Scientific reports
Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical ...

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Proceedings of the National Academy of Sciences of the United States of America
Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic proces...

FateNet: an integration of dynamical systems and deep learning for cell fate prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and ...

GenoM7GNet: An Efficient N-Methylguanosine Site Prediction Approach Based on a Nucleotide Language Model.

IEEE/ACM transactions on computational biology and bioinformatics
N-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanw...