AIMC Topic: RNA-Seq

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Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.

Scientific reports Jul 2, 2025
In drug discovery, different data modalities (chemical structure, cell biology, quantum mechanics, etc.) are abundant, and their integration can help with understanding aspects of chemistry, biology, and their interactions. Within cell biology, cell ...
Transcriptome RNA-Seq Cluster Analysis Humans Machine Learning Drug Discovery Gene Expression Profiling
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Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.

Nature communications Jul 1, 2025
During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq d...
Humans Machine Learning Gene Expression Profiling Gene Regulatory Networks Signal Transduction Transcription Factors Cell Communication Transcriptome Single-Cell Analysis RNA-Seq
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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 Humans Machine Learning Single-Cell Analysis
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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.
Sequence Analysis, RNA Humans Animals Gene Expression Profiling Single-Cell Analysis RNA-Seq Transcriptome Deep Learning
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Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction.

JCO clinical cancer informatics May 28, 2025
PURPOSE: Recent advances in machine learning have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemia (ALL) using RNA-sequencing (RNA-seq) data. Although these models have shown promising results, the...
RNA-Seq Computational Biology Humans Precursor Cell Lymphoblastic Leukemia-Lymphoma Machine Learning Biomarkers, Tumor
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Identification of mitophagy-related biomarkers in severe acute pancreatitis: integration of WGCNA, machine learning algorithms and scRNA-seq.

Frontiers in immunology May 28, 2025
BACKGROUND: Mitophagy is a highly conserved cellular process in eukaryotic cells that selectively clears dysfunctional or damaged mitochondria through autophagy mechanisms to maintain mitochondrial homeostasis. However, the role of mitophagy in the p...
Gene Expression Profiling Single-Cell Analysis RNA-Seq Algorithms Biomarkers Animals Computational Biology Disease Models, Animal Pancreatitis Mitophagy Gene Regulatory Networks Single-Cell Gene Expression Analysis Mice Humans Machine Learning
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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...
RNA-Seq Single-Cell Gene Expression Analysis Sequence Analysis, RNA Software Autoencoder Algorithms Single-Cell Analysis Humans Neural Networks, Computer
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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...
Gene Regulatory Networks Deep Learning Sequence Analysis, RNA Single-Cell Gene Expression Analysis Single-Cell Analysis Computational Biology RNA-Seq Humans Machine Learning Algorithms
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Molecular features and diagnostic modeling of synovium- and IPFP-derived OA macrophages in the inflammatory microenvironment via scRNA-seq and machine learning.

Journal of orthopaedic surgery and research Apr 17, 2025
BACKGROUND: Osteoarthritis (OA) is the leading cause of degenerative joint disease, with total joint replacement as the only definitive cure. However, no disease-modifying therapy is currently available. Inflammation and fibrosis in the infrapatellar...
Male Adipose Tissue Middle Aged Osteoarthritis Single-Cell Analysis Humans Cellular Microenvironment Inflammation Macrophages Machine Learning Synovial Membrane Female Rats, Sprague-Dawley Animals RNA-Seq Single-Cell Gene Expression Analysis Rats
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Assessing concordance between RNA-Seq and NanoString technologies in Ebola-infected nonhuman primates using machine learning.

BMC genomics Apr 10, 2025
This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). A detailed comparison of both platforms revealed a strong co...
Gene Expression Profiling Dendritic Cells RNA-Seq Ebolavirus Hemorrhagic Fever, Ebola 2',5'-Oligoadenylate Synthetase Humans Machine Learning Animals
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