ErbB Receptors

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS 39639305

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...

Animals Antineoplastic Agents Apoptosis Carcinoma, Non-Small-Cell Lung Cell Line, Tumor Cell Proliferation ErbB Receptors Humans Lung Neoplasms Machine Learning Mice Mice, Nude Mutation Oleanolic Acid Protein Kinase Inhibitors Signal Transduction Xenograft Model Antitumor Assays

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Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

PloS one 40168285

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...

Aged Carcinoma, Non-Small-Cell Lung ErbB Receptors Female Humans Lung Neoplasms Machine Learning Male Middle Aged Postoperative Complications Retrospective Studies Risk Factors ROC Curve Venous Thromboembolism

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[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR  Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Zhongguo fei ai za zhi = Chinese journal of lung cancer 40114487

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic facto...

Adult Aged Biomarkers, Tumor Carcinoma, Non-Small-Cell Lung ErbB Receptors Female Humans Lung Neoplasms Machine Learning Male Middle Aged Mutation Prognosis Retrospective Studies

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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 39965912

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...

Deep Learning Drug Discovery ErbB Receptors Humans Ligands Neural Networks, Computer Protein Binding Proteins SARS-CoV-2 Uncertainty

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Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.

Journal of biomedical informatics 40122479

OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.

Anaplastic Lymphoma Kinase Biological Ontologies Data Accuracy Data Warehousing ErbB Receptors Humans Lung Neoplasms Mutation Phenotype Protein Kinase Inhibitors

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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 40075375

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...

Adenocarcinoma of Lung Adult Aged Brain Neoplasms Deep Learning ErbB Receptors Female Humans Lung Neoplasms Magnetic Resonance Imaging Male Middle Aged Mutation Radiomics Retrospective Studies ROC Curve

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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 40053865

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...

Allosteric Regulation ErbB Receptors Humans Machine Learning Molecular Dynamics Simulation Mutation Protein Kinase Inhibitors Quantitative Structure-Activity Relationship

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Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Journal of translational medicine 40301933

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma. However, not all patients with EGFR mutations respond to TKIs. This study...

Adenocarcinoma of Lung Aged Deep Learning ErbB Receptors Female Humans Lung Neoplasms Male Middle Aged Molecular Targeted Therapy Mutation Protein Kinase Inhibitors Retrospective Studies Tomography, X-Ray Computed

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Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Nature communications 40234405

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...

Carcinoma, Renal Cell Computational Biology Deep Learning ErbB Receptors Genetic Predisposition to Disease Humans Kidney Neoplasms Markov Chains Reinforcement Machine Learning Risk Factors

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Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BMC cancer 40312289

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung can...

Brain Neoplasms Carcinoma, Non-Small-Cell Lung ErbB Receptors Humans Lung Neoplasms Machine Learning Mutation Prognosis

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR  Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

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.

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

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