AIMC Topic: ErbB Receptors

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Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.

Medical physics Apr 1, 2025
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor receptor (EGFR) mutations play a pivotal role as indicators for therapeutic intervention with targeted...
Lung Neoplasms Positron Emission Tomography Computed Tomography Mutation ErbB Receptors Male Middle Aged Image Processing, Computer-Assisted Adenocarcinoma of Lung Humans Retrospective Studies Female Aged Radiomics
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Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

PloS one Apr 1, 2025
BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the VTE risk factors and established a machine-learning model to predict a failure of postoperative throm...
Humans Machine Learning Retrospective Studies Female Risk Factors Venous Thromboembolism ErbB Receptors Postoperative Complications ROC Curve Lung Neoplasms Carcinoma, Non-Small-Cell Lung Aged Male Middle Aged
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Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.

Journal of biomedical informatics Mar 21, 2025
OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.
Mutation Data Accuracy Biological Ontologies ErbB Receptors Protein Kinase Inhibitors Data Warehousing Anaplastic Lymphoma Kinase Humans Phenotype Lung Neoplasms
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Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BMC cancer Mar 12, 2025
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...
Aged Adult Radiomics Brain Neoplasms Humans Retrospective Studies Female Lung Neoplasms Magnetic Resonance Imaging Mutation ErbB Receptors Male Middle Aged Deep Learning ROC Curve Adenocarcinoma of Lung
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Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

The journal of physical chemistry. B Mar 7, 2025
Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...
Protein Kinase Inhibitors Allosteric Regulation Mutation Molecular Dynamics Simulation Quantitative Structure-Activity Relationship ErbB Receptors Humans Machine Learning
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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling Feb 18, 2025
There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...
Uncertainty Proteins Ligands ErbB Receptors Humans Neural Networks, Computer SARS-CoV-2 Drug Discovery Deep Learning Protein Binding
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GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery.

Molecular diversity Jan 10, 2025
Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fix...
Deep Learning Humans Protein Binding Neural Networks, Computer Alzheimer Disease Drug Discovery ErbB Receptors
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS Dec 5, 2024
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...
Carcinoma, Non-Small-Cell Lung Oleanolic Acid Humans Mice, Nude Xenograft Model Antitumor Assays Machine Learning Mice Apoptosis ErbB Receptors Animals Cell Proliferation Mutation Protein Kinase Inhibitors Antineoplastic Agents Signal Transduction Cell Line, Tumor Lung Neoplasms
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Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models.

Bio Systems Nov 7, 2024
The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of ...
ErbB Receptors Computer Simulation Humans Signal Transduction Machine Learning Models, Biological Algorithms MAP Kinase Signaling System
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Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Computers in biology and medicine Oct 25, 2024
Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive...
Receptors, Estrogen Receptors, Progesterone Humans NF-kappa B Machine Learning Ligands Female Receptor, ErbB-2 Breast Neoplasms ErbB Receptors Molecular Docking Simulation Databases, Protein
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