AIMC Topic: Sequence Analysis, RNA

Clear Filters Showing 121 to 130 of 361 articles

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

The Journal of molecular diagnostics : JMD
This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was develope...

scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.

BMC genomics
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose...

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities.

The Journal of biological chemistry
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding R...

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Exist...

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

International journal of molecular sciences
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations...

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Cell reports methods
Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined anno...

DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics.

Genome biology
Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph...

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review.

Medical oncology (Northwood, London, England)
Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the appl...

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Genome biology
Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) da...

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method.

Journal of computational biology : a journal of computational molecular cell biology
Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cel...