AIMC Topic: Transcriptome
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Novel Transcriptomic Signatures in Fibrostenotic Crohn's Disease: Dysregulated Pathways, Promising Biomarkers, and Putative Therapeutic Targets.
Inflammatory bowel diseases
Jun 13, 2025
BACKGROUND: Fibrosis is a common complication in Crohn's disease (CD), often leading to intestinal strictures. This study aims to explore the transcriptomic signature of fibrostenotic ileal CD for a comprehensive characterization of biological and ce...
Molecular landscape of endometrioid Cancer: Integrating multiomics and deep learning for personalized survival prediction.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.
Cancer type and survival prediction based on transcriptomic feature map.
Computers in biology and medicine
Jun 1, 2025
This study achieved cancer type and survival time prediction by transforming transcriptomic features into feature maps and employing deep learning models. Using transcriptomic data from 27 cancer types and survival data from 10 types in the TCGA data...
Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Jun 1, 2025
Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing th...
Exploring pesticide risk in autism via integrative machine learning and network toxicology.
Ecotoxicology and environmental safety
Jun 1, 2025
Autism Spectrum Disorder (ASD) is a prevalent neurodevelopmental condition influenced by both genetic and environmental factors, including pesticide exposure. This study aims to investigate the pathogenic mechanisms of ASD and identify potential caus...
Upregulation of immune genes in the proliferative phase endometrium enables classification into women with recurrent pregnancy loss versus controls.
Human reproduction (Oxford, England)
Jun 1, 2025
STUDY QUESTION: Does the transcriptome of preconceptional endometrium in the proliferative phase show a specific profile in women with recurrent pregnancy loss (RPL)?
Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.
Interdisciplinary sciences, computational life sciences
Jun 1, 2025
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (...
Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significant risks for liver fibrosis and cardiovascular complications. Despite extensive research, reliable b...
scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.
IEEE journal of biomedical and health informatics
Jun 1, 2025
Single-cell multi-omics sequencing technology comprehensively considers various molecular features to reveal the complexity of cells information. The clustering analysis of multi-omics data provides new insight into cellular heterogeneity. However, m...
Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.
Scientific reports
May 31, 2025
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice h...
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