AIMC Topic: Gene Expression Regulation, Neoplastic

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Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

Journal of cellular and molecular medicine Jul 1, 2024
The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cel...
Adenocarcinoma of Lung Single-Cell Analysis Lung Neoplasms Immunotherapy Gene Expression Regulation, Neoplastic Humans Machine Learning Prognosis Tumor Microenvironment Biomarkers, Tumor Disease Progression Gene Expression Profiling
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Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Journal of cellular and molecular medicine Jul 1, 2024
Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mR...
Humans Machine Learning Prognosis Multiomics Gene Expression Regulation, Neoplastic MicroRNAs Adenocarcinoma of Lung Biomarkers, Tumor Gene Expression Profiling Lung Neoplasms Immunotherapy
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Study of prognostic splicing factors in cancer using machine learning approaches.

Human molecular genetics Jun 21, 2024
Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing ...
Neoplasms Liver Neoplasms Carcinoma, Hepatocellular Computational Biology Humans Machine Learning Prognosis Biomarkers, Tumor Alternative Splicing RNA Splicing Factors RNA, Messenger RNA-Binding Proteins Gene Expression Regulation, Neoplastic RNA Splicing
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Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

Neuro-oncology Jun 3, 2024
BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, p...
Humans Machine Learning Craniopharyngioma Child Phenotype Gene Expression Regulation, Neoplastic Female Pituitary Neoplasms Biomarkers, Tumor Cellular Senescence Male
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Validating linalool as a potential drug for breast cancer treatment based on machine learning and molecular docking.

Drug development research Jun 1, 2024
Breast cancer (BC) is a common cancer for women. This study aims to construct a prognostic risk model of BC and identify prognostic biomarkers through machine learning approaches, and clarify the mechanism by which linalool exerts tumor-suppressive f...
Molecular Docking Simulation Machine Learning Antineoplastic Agents Prognosis Cell Line, Tumor Cell Proliferation Humans Female Gene Expression Regulation, Neoplastic Apoptosis Breast Neoplasms Acyclic Monoterpenes
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Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Journal of cellular and molecular medicine Jun 1, 2024
Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO...
Transcriptome ROC Curve Gene Expression Regulation, Neoplastic Nomograms Glioma Lymphocytes, Tumor-Infiltrating Brain Neoplasms Cell Death Male Myeloid Differentiation Factor 88 Middle Aged Humans Gene Expression Profiling Machine Learning Prognosis Female Biomarkers, Tumor
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ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Briefings in bioinformatics May 23, 2024
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, ar...
Computational Biology Gene Expression Regulation, Neoplastic Survival Analysis Breast Neoplasms Neoplasms Humans Gene Expression Profiling Neural Networks, Computer Deep Learning Algorithms Female
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Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning.

Journal of cellular and molecular medicine May 1, 2024
This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our ap...
Machine Learning Prognosis Female Aged Biomarkers, Tumor Male Middle Aged Humans Gene Expression Regulation, Neoplastic Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Cyclin-Dependent Kinase Inhibitor p16
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Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Technology in cancer research & treatment Jan 1, 2024
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and ...
Gene Ontology Gene Expression Profiling Kidney Neoplasms Female Humans Computational Biology Proportional Hazards Models Biomarkers, Tumor Machine Learning Gene Expression Regulation, Neoplastic Kaplan-Meier Estimate Male Algorithms Mutation Carcinoma, Renal Cell Middle Aged Prognosis
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Molecular characterization, immunocorrelation analysis, WGCNA analysis and machine learning modeling of genes associated with copper death subtypes of laryngeal cancer.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Laryngeal cancer is a malignant tumor that originates from the mucous membrane of the larynx. Currently, the specific involvement mechanism of copper death in laryngeal cancer patients has not been deeply studied.
Gene Expression Profiling Transcriptome Gene Expression Regulation, Neoplastic Copper Humans Laryngeal Neoplasms Machine Learning Copper-Transporting ATPases Prognosis Copper Transporter 1 HSP47 Heat-Shock Proteins
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