AIMC Topic: Cell Proliferation

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Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach.

Frontiers in immunology Oct 30, 2024
INTRODUCTION: Lung cancer is a leading cause of cancer-related deaths, with its incidence continuing to rise. Chromatin remodeling, a crucial process in gene expression regulation, plays a significant role in the development and progression of malign...
Lung Neoplasms Trefoil Factor-1 Mice Multiomics Gene Expression Regulation, Neoplastic Cell Proliferation Apoptosis Xenograft Model Antitumor Assays Adenocarcinoma of Lung Chromatin Tumor Microenvironment Biomarkers, Tumor Humans Animals Machine Learning Cell Line, Tumor Prognosis Female
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Ultrasound-based artificial intelligence model for prediction of Ki-67 proliferation index in soft tissue tumors.

Academic radiology Oct 15, 2024
RATIONALE AND OBJECTIVES: To investigate the value of deep learning (DL) combined with radiomics and clinical and imaging features in predicting the Ki-67 proliferation index of soft tissue tumors (STTs).
Humans Artificial Intelligence Retrospective Studies Female Deep Learning Ultrasonography Cell Proliferation Soft Tissue Neoplasms Ki-67 Antigen Aged Adult Male Middle Aged
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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...
Structure-Activity Relationship Molecular Structure Protein Kinase Inhibitors Cell Cycle Proteins Dose-Response Relationship, Drug Protein-Tyrosine Kinases Decision Making Drug Screening Assays, Antitumor Humans Antineoplastic Agents Deep Learning Cell Line, Tumor Cell Proliferation
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Deep learning reveals a damage signalling hierarchy that coordinates different cell behaviours driving wound re-epithelialisation.

Development (Cambridge, England) Sep 24, 2024
One of the key tissue movements driving closure of a wound is re-epithelialisation. Earlier wound healing studies describe the dynamic cell behaviours that contribute to wound re-epithelialisation, including cell division, cell shape changes and cell...
Re-Epithelialization Cell Shape Wings, Animal Calcium Signaling Pupa Animals Macrophages Deep Learning Cell Proliferation Signal Transduction Wound Healing Cell Movement Epithelium Drosophila Proteins Drosophila melanogaster
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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 Neoplasms Mitochondria Esophageal Squamous Cell Carcinoma Apoptosis Regulatory Proteins Mitochondrial Proteins Single-Cell Gene Expression Analysis Gene Expression Regulation, Neoplastic Cell Proliferation Cell Movement Mitophagy Humans Machine Learning Algorithms Prognosis Biomarkers, Tumor Transcriptome Cell Line, Tumor Single-Cell Analysis RNA-Seq
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Low-pass whole genome sequencing of circulating tumor cells to evaluate chromosomal instability in triple-negative breast cancer.

Scientific reports Sep 3, 2024
Chromosomal Instability (CIN) is a common and evolving feature in breast cancer. Large-scale Transitions (LSTs), defined as chromosomal breakages leading to gains or losses of at least 10 Mb, have recently emerged as a metric of CIN due to their stan...
Reproducibility of Results Cell Line, Tumor Triple Negative Breast Neoplasms Cell Proliferation DNA Copy Number Variations Humans Machine Learning Female DNA Repair Predictive Learning Models Chromosomal Instability Area Under Curve Neoplastic Cells, Circulating Whole Genome Sequencing Feasibility Studies
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Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.

Molecules (Basel, Switzerland) Jul 2, 2024
In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this pur...
Molecular Dynamics Simulation Sulfonamides Triazoles Quinolines Humans Antineoplastic Agents Machine Learning Cell Line, Tumor Molecular Docking Simulation Cell Proliferation Structure-Activity Relationship Molecular Structure
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From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

ChemMedChem Jun 20, 2024
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...
Antineoplastic Agents Deep Learning Cell Line, Tumor Cell Proliferation Dose-Response Relationship, Drug Drug Screening Assays, Antitumor Structure-Activity Relationship Molecular Structure Vascular Endothelial Growth Factor Receptor-2 Humans Molecular Docking Simulation Drug Discovery Protein Kinase Inhibitors
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Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

Journal of chemical information and modeling Jun 7, 2024
The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus p...
Animals Molecular Docking Simulation Drug Discovery Antineoplastic Agents Deep Learning Lymphocyte Specific Protein Tyrosine Kinase p56(lck) Humans Cell Line, Tumor Mice Cell Proliferation Protein Kinase Inhibitors
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Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer.

Journal of cancer research and clinical oncology May 20, 2024
BACKGROUND: Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research ai...
Tumor Microenvironment Biomarkers, Tumor Cell Line, Tumor Gene Expression Regulation, Neoplastic Cell Proliferation Humans Machine Learning Prognosis Urinary Bladder Neoplasms Drug Resistance, Neoplasm Smad6 Protein
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