AIMC Topic: Sequence Analysis, RNA

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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 Nucleic Acid Conformation Software RNA, Untranslated Deep Learning
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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 ...
Humans Machine Learning Biomarkers, Tumor Prognosis Cell Movement Sequence Analysis, RNA Single-Cell Analysis Female Cancer-Associated Fibroblasts Lung Neoplasms Gene Expression Regulation, Neoplastic Adenocarcinoma of Lung Cell Proliferation
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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.
Mice Sequence Analysis, RNA Support Vector Machine Gene Expression Profiling Sepsis Single-Cell Analysis Databases, Genetic Humans Machine Learning Animals Lung
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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...
Cluster Analysis Principal Component Analysis RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA Machine Learning Single-Cell Analysis Humans
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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 Animals RNA-Seq Transcriptome Gene Expression Profiling Sequence Analysis, RNA Deep Learning Single-Cell Analysis
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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...
Single-Cell Analysis Neural Networks, Computer RNA-Seq Single-Cell Gene Expression Analysis Humans Sequence Analysis, RNA Software Autoencoder Algorithms
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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 Sequence Analysis, RNA Graph Neural Networks Humans Machine Learning Neural Networks, Computer Algorithms Single-Cell Analysis
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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...
Lung Deep Learning Single-Cell Analysis Algorithms Animals Supervised Machine Learning Mice Computational Biology Sequence Analysis, RNA
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Transfer learning of multicellular organization via single-cell and spatial transcriptomics.

PLoS computational biology Apr 21, 2025
Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) m...
Transcriptome Machine Learning Single-Cell Analysis Computational Biology Mice Animals Humans Sequence Analysis, RNA Gene Expression Profiling Neural Networks, Computer Drosophila
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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

BMC bioinformatics Apr 18, 2025
BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug d...
Deep Learning Single-Cell Analysis RNA-Seq Humans Machine Learning Algorithms Sequence Analysis, RNA Gene Regulatory Networks Single-Cell Gene Expression Analysis Computational Biology
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