AIMC Topic: Sequence Analysis, RNA

Clear Filters Showing 11 to 20 of 330 articles

Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.

BMC bioinformatics Jul 1, 2025
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression by allowing researchers to analyze the transcriptomes of individual cells. This technology provides unprecedented insights into cellular heterogeneity, cellular st...
Sequence Analysis, RNA Molecular Sequence Annotation Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Humans Machine Learning Algorithms
View on PubMed DOI

Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysis.

Scientific reports Jul 1, 2025
Periodontitis, a chronic inflammatory condition of the periodontium, is associated with over 60 systemic diseases. Despite advancements, precision medicine approaches have had limited success, emphasizing the need for deeper insights into cellular su...
Humans Single-Cell Analysis Sequence Analysis, RNA Principal Component Analysis Algorithms Gene Expression Profiling Transcriptome Autoencoder Keratinocytes Gingiva
View on PubMed DOI

RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.

Nature communications Jul 1, 2025
While RNA has recently been recognized as an interesting small-molecule drug target, many challenges remain to be addressed before we take full advantage of it. This emphasizes the necessity to improve our understanding of its structures and function...
Computational Biology Sequence Analysis, RNA RNA Software Nucleic Acid Conformation Deep Learning RNA, Untranslated
View on PubMed DOI

Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing.

Scientific reports Jul 1, 2025
Cancer-associated fibroblasts (CAFs) play important roles in the progression of lung adenocarcinoma (LUAD). We examined CAF subgroups via gene ontology, pseudo-time, and cell communication analyses and explored their prognostic value in LUAD using a ...
Biomarkers, Tumor Single-Cell Analysis Lung Neoplasms Machine Learning Prognosis Female Humans Sequence Analysis, RNA Cell Movement Gene Expression Regulation, Neoplastic Cancer-Associated Fibroblasts Cell Proliferation Adenocarcinoma of Lung
View on PubMed DOI

Identification of key genes and development of an identifying machine learning model for sepsis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.] Jun 30, 2025
OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ single-cell RNA sequencing (scRNA-seq) and machine learning.
Single-Cell Analysis Mice Sequence Analysis, RNA Support Vector Machine Lung Humans Databases, Genetic Gene Expression Profiling Machine Learning Sepsis Animals
View on PubMed DOI

scGANSL: Graph Attention Network with Subspace Learning for scRNA-seq Data Clustering.

Journal of chemical information and modeling Jun 5, 2025
Single-cell RNA sequencing (scRNA-seq) has become a crucial technology for analyzing cellular diversity at the single-cell level. Cell clustering is crucial in scRNA-seq data analysis as it accurately identifies distinct cell types and uncovers poten...
Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA Cluster Analysis Principal Component Analysis Humans Machine Learning
View on PubMed DOI

SC2Spa: a deep learning based approach to map transcriptome to spatial origins at cellular resolution.

BMC bioinformatics Jun 2, 2025
BACKGROUND: Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates.
Humans Single-Cell Analysis Animals RNA-Seq Gene Expression Profiling Sequence Analysis, RNA Transcriptome Deep Learning
View on PubMed DOI

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling.

Biology direct May 27, 2025
BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) technology reveals biological processes and molecular-level genomic information among individual cells. Numerous computational methods, including methods based on graph neural networks (GNNs), have b...
Algorithms Single-Cell Analysis RNA-Seq Autoencoder Humans Single-Cell Gene Expression Analysis Neural Networks, Computer Sequence Analysis, RNA Software
View on PubMed DOI

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

IEEE transactions on neural networks and learning systems May 2, 2025
Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs ...
Gene Regulatory Networks Machine Learning Humans Algorithms Sequence Analysis, RNA Neural Networks, Computer Single-Cell Analysis Graph Neural Networks
View on PubMed DOI

TransAnno-Net: A Deep Learning Framework for Accurate Cell Type Annotation of Mouse Lung Tissue Using Self-supervised Pretraining.

Computer methods and programs in biomedicine Apr 24, 2025
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has become a significant tool for addressing complex issuess in the field of biology. In the context of scRNA-seq analysis, it is imperative to accurately determine the type of each cell. However, co...
Computational Biology Sequence Analysis, RNA Supervised Machine Learning Deep Learning Single-Cell Analysis Mice Animals Lung Algorithms
View on PubMed DOI
Popular Topics
Recent Journals