AIMC Topic: Biomarkers, Tumor
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Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.
International journal of molecular sciences
Apr 13, 2025
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Analysis of the Relationship Between and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.
International journal of medical sciences
Apr 13, 2025
This study measures expression of () and related cytokine genes in bone marrow mononuclear cells in patients with hematological malignancies, analyzing the relationship between them with an integrated framework of statistical analyses, machine learn...
Tumor-educated platelets in lung cancer.
Clinica chimica acta; international journal of clinical chemistry
Apr 12, 2025
Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-related death worldwide. The new diagnostic biomarkers called tumour-educated platelets (TEPs) show s...
Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.
Scientific reports
Apr 12, 2025
Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the...
DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Apr 11, 2025
The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for cancer detection. Enzyme-free signal amplification DNA circuits have facilitated the detection of low-abundance miRNAs. Howeve...
Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.
Scientific reports
Apr 11, 2025
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...
Integrative Multi-Omics Analysis Reveals Molecular Subtypes of Ovarian Cancer and Constructs Prognostic Models.
Journal of immunotherapy (Hagerstown, Md. : 1997)
Apr 9, 2025
Ovarian cancer (OV) remains the most lethal gynecological malignancy. The aim of this study was to identify molecular subtypes of OV through integrative multi-omics analysis and construct machine learning-based prognostic models for predicting the ef...
Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.
BMC cancer
Apr 9, 2025
BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...
Carcinogenesis
Vimentin
Lysine
Sequence Analysis, RNA
Gene Expression Regulation, Neoplastic
Nomograms
Cell Proliferation
RNA-Seq
Gene Regulatory Networks
Single-Cell Gene Expression Analysis
Computational Biology
Humans
Machine Learning
Prognosis
Stomach Neoplasms
Biomarkers, Tumor
Cell Line, Tumor
Single-Cell Analysis
Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.
BMC cancer
Apr 9, 2025
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...
Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.
BMC cancer
Apr 7, 2025
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...
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