ErbB Receptors

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

The journal of pathology. Clinical research Nov 1, 2024

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitati...

Humans Female Image Interpretation, Computer-Assisted DNA Mutational Analysis ErbB Receptors Adenocarcinoma of Lung Deep Learning Lung Neoplasms Mutation

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D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

Briefings in bioinformatics Mar 27, 2024

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recomm...

ErbB Receptors Carcinoma, Non-Small-Cell Lung Mutation Information Storage and Retrieval Humans Deep Learning Lung Neoplasms

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Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England) Dec 22, 2021

MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...

MCF-7 Cells Epithelial-Mesenchymal Transition Breast Neoplasms ErbB Receptors Deep Learning Humans Cell Line, Tumor Female

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Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Feb 1, 2021

BACKGROUND: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor ...

Imaging Genomics Image Interpretation, Computer-Assisted Fluorodeoxyglucose F18 ErbB Receptors Aged Predictive Value of Tests Lung Humans Sensitivity and Specificity Positron Emission Tomography Computed Tomography Mutation Lung Neoplasms Male Retrospective Studies Machine Learning Radiopharmaceuticals Carcinoma, Non-Small-Cell Lung Female Reproducibility of Results

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Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Cell reports Jan 19, 2021

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode s...

Machine Learning Deep Learning Signal Transduction Drug Resistance, Neoplasm Humans Neural Networks, Computer ErbB Receptors

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Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation.

Journal of chemical information and modeling Oct 26, 2020

Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational ...

Molecular Docking Simulation Support Vector Machine Quantitative Structure-Activity Relationship Machine Learning ErbB Receptors

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Using machine learning to improve ensemble docking for drug discovery.

Proteins Oct 1, 2020

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activ...

Drug Discovery User-Computer Interface Protein Binding Support Vector Machine Bayes Theorem Ligands Humans Molecular Docking Simulation Protein Structure, Secondary ErbB Receptors Protein Kinase Inhibitors Binding Sites

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TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Chemical biology & drug design Sep 1, 2020

The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant muta...

Datasets as Topic ErbB Receptors Humans Machine Learning Models, Theoretical Mutation

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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology Aug 1, 2020

PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...

ErbB Receptors Proto-Oncogene Proteins p21(ras) Positron-Emission Tomography Algorithms High-Throughput Nucleotide Sequencing Female Multivariate Analysis Aged Multimodal Imaging Humans Genomics Area Under Curve Machine Learning Carcinoma, Non-Small-Cell Lung Mutation Male Lung Neoplasms

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Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

Journal of cancer research and clinical oncology Mar 1, 2020

PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making.

Lung Neoplasms Mutation ErbB Receptors Decision Making, Computer-Assisted Aged Humans Male Neural Networks, Computer Middle Aged Female

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

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation.

Using machine learning to improve ensemble docking for drug discovery.

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors.

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

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