AIMC Topic: Transcriptome
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Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia.
BMC cancer
Feb 15, 2025
BACKGROUND: Dysregulation or abnormality of the programmed cell death (PCD) pathway is closely related to the occurrence and development of many tumors, including acute myeloid leukemia (AML). Studying the abnormal characteristics of PCD pathway-rela...
Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization.
BMC cancer
Feb 13, 2025
BACKGROUND: Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, especially remain elusive in the context of lung adenocarcinoma. Through the integration of multiple omic...
X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development.
Computers in biology and medicine
Feb 12, 2025
In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This...
Deciphering the role of cuproptosis in the development of intimal hyperplasia in rat carotid arteries using single cell analysis and machine learning techniques.
Scientific reports
Feb 12, 2025
UNLABELLED: This study aims to explore the regulatory role of cuproptosis in carotid intimal hyperplasia (IH), providing new insights into its pathophysiological mechanisms and potential diagnostic and therapeutic strategies.
Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.
Scientific reports
Feb 11, 2025
Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large om...
Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE.
Small methods
Feb 4, 2025
Spatially resolved transcriptomics (SRT) is poised to advance the understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous c...
Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.
Scientific reports
Feb 4, 2025
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.
Immunologic research
Feb 4, 2025
Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging outcomes in a subset of HCC patients. The main challenges in checkpoint immunotherapy for HCC are to e...
Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.
International journal of molecular sciences
Jan 30, 2025
The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunodeficiencies, and cancers. The vaccinia virus Tiantan strain (VVTT) has been instrumental as an irrepl...
ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing.
Computers in biology and medicine
Jan 29, 2025
Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challengi...
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