AIMC Topic: Cell Line, Tumor

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Machine learning identification of NK cell immune characteristics in hepatocellular carcinoma based on single-cell sequencing and bulk RNA sequencing.

Genes & genomics Oct 21, 2024
BACKGROUND: Hepatocellular carcinoma (HCC) is a highly malignant tumor; however, its immune microenvironment and mechanisms remain elusive. Single-cell sequencing allows for the exploration of immune characteristics within tumor at the cellular level...
Biomarkers, Tumor Male Middle Aged Cell Line, Tumor Machine Learning Prognosis Female Tumor Microenvironment Gene Expression Regulation, Neoplastic Killer Cells, Natural Liver Neoplasms Carcinoma, Hepatocellular Single-Cell Analysis Sequence Analysis, RNA Humans
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Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 21, 2024
A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate can...
Antineoplastic Agents Cell Line, Tumor Metabolomics Chromatography, Liquid Humans Prostatic Neoplasms Machine Learning Tandem Mass Spectrometry Male Energy Metabolism Drug Discovery
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Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning.

Biochimica et biophysica acta. Molecular basis of disease Oct 5, 2024
BACKGROUND: Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.
Humans Machine Learning Prognosis Epithelial Cells Receptor, Notch3 Female Cell Line, Tumor Colorectal Neoplasms Single-Cell Analysis Male Gene Expression Regulation, Neoplastic Transcriptome Cancer-Associated Fibroblasts
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Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Oct 4, 2024
Cell misuse and cross-contamination pose a significant threat to the accuracy of cell research outcomes, often leading to the wasteful expenditure of time, manpower, and material resources. Consequently, the accurate identification of cell lines is p...
Humans Neural Networks, Computer Algorithms Neoplasms Cell Line, Tumor Spectrum Analysis, Raman
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Salidroside sensitizes Triple-negative breast cancer to ferroptosis by SCD1-mediated lipogenesis and NCOA4-mediated ferritinophagy.

Journal of advanced research Sep 29, 2024
INTRODUCTION: Triple-negative breast cancer (TNBC) is the primary cause of breast cancer-induced death in women. Literature has confirmed the benefits of Salidroside (Sal) in treating TNBC. However, the study about potential therapeutic targets and m...
Mice Triple Negative Breast Neoplasms Signal Transduction Mice, Nude Humans Female Animals Cell Line, Tumor Ferritins Xenograft Model Antitumor Assays Stearoyl-CoA Desaturase Lipogenesis Nuclear Receptor Coactivators Ferroptosis Phenols Autophagy Glucosides
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Wee1 inhibitor optimization through deep-learning-driven decision making.

European journal of medicinal chemistry Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Decision Making Drug Screening Assays, Antitumor Structure-Activity Relationship Molecular Structure Cell Line, Tumor Cell Proliferation Dose-Response Relationship, Drug Protein-Tyrosine Kinases Humans Protein Kinase Inhibitors Antineoplastic Agents Cell Cycle Proteins Deep Learning
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Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

Frontiers in immunology Sep 24, 2024
INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for imp...
Gene Expression Regulation, Neoplastic Glioma Brain Neoplasms Carcinogenesis Tumor Escape Humans Oncogenes Animals Cell Line, Tumor Machine Learning Mice Prognosis Biomarkers, Tumor
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Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms.

Journal of microbiology and biotechnology Sep 23, 2024
As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively removes damaged mitochondria and is crucial for cellular homeostasis. While tumor development and re...
Esophageal Squamous Cell Carcinoma Apoptosis Regulatory Proteins Mitochondrial Proteins Algorithms Mitophagy Prognosis Cell Proliferation Biomarkers, Tumor Single-Cell Analysis Cell Movement Transcriptome RNA-Seq Esophageal Neoplasms Cell Line, Tumor Single-Cell Gene Expression Analysis Mitochondria Gene Expression Regulation, Neoplastic Humans Machine Learning
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Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.

Scientific reports Sep 13, 2024
Cancer stem cells (CSCs) have the potential to self-renew and induce cancer, which may contribute to a poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC-...
Kinesins Humans Machine Learning Cell Line, Tumor Algorithms Female Gene Expression Regulation, Neoplastic Prognosis Tumor Microenvironment Triple Negative Breast Neoplasms Biomarkers, Tumor Neoplastic Stem Cells Gene Expression Profiling
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Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics.

Nanoscale Sep 12, 2024
How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic ...
Anthocyanins Metals, Rare Earth Bevacizumab Theranostic Nanomedicine Humans Machine Learning Female Breast Neoplasms Animals Cell Line, Tumor Mice Nanoparticles
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