AIMC Topic: ErbB Receptors

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Using machine learning to improve ensemble docking for drug discovery.

Proteins May 25, 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...
Humans Molecular Docking Simulation Drug Discovery Ligands ErbB Receptors Protein Kinase Inhibitors Binding Sites Protein Structure, Secondary User-Computer Interface Protein Binding Support Vector Machine Bayes Theorem
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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 17, 2020
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 ...
ErbB Receptors Imaging Genomics Reproducibility of Results Mutation Aged Humans Radiopharmaceuticals Sensitivity and Specificity Image Interpretation, Computer-Assisted Machine Learning Male Fluorodeoxyglucose F18 Retrospective Studies Lung Female Lung Neoplasms Carcinoma, Non-Small-Cell Lung Predictive Value of Tests Positron Emission Tomography Computed Tomography
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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 Dec 5, 2019
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.
Neural Networks, Computer Female Aged Male Middle Aged Humans Lung Neoplasms Mutation ErbB Receptors Decision Making, Computer-Assisted
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Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.

Scientific reports Nov 28, 2019
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of m...
Adult Breast Neoplasms Middle Aged Neoplasm Invasiveness Neoplasm Grading Tomography, X-Ray Computed Female Aged Aged, 80 and over ErbB Receptors Receptors, Estrogen Ki-67 Antigen Humans Machine Learning
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Cross-registry neural domain adaptation to extract mutational test results from pathology reports.

Journal of biomedical informatics Aug 8, 2019
OBJECTIVE: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational te...
Lung Neoplasms Computational Biology Carcinoma, Non-Small-Cell Lung Mutation Registries Data Mining ErbB Receptors Neural Networks, Computer Machine Learning Neoplasms Deep Learning Humans
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Recognition of early and late stages of bladder cancer using metabolites and machine learning.

Metabolomics : Official journal of the Metabolomic Society Jun 20, 2019
INTRODUCTION: Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and ...
Glycine Biomarkers, Tumor Galactose Humans Insulin ErbB Receptors Metabolic Networks and Pathways ROC Curve Machine Learning Area Under Curve Urinary Bladder Neoplasms Software Neoplasm Staging Amino Acids Proto-Oncogene Proteins c-akt
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Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.

Cancer medicine May 10, 2019
To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR-mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild-type...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed Female Reproducibility of Results Male Deep Learning ROC Curve Image Processing, Computer-Assisted Humans Data Curation Computational Biology Adenocarcinoma of Lung Neoplasm Staging Mutation ErbB Receptors
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Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning.

Journal of the American Chemical Society Apr 18, 2019
The stoichiometry of protein complexes is precisely regulated in cells and is fundamental to protein function. Singe-molecule fluorescence imaging based photobleaching event counting is a new approach for protein stoichiometry determination under phy...
Single Molecule Imaging Protein Structure, Quaternary Photobleaching Humans HeLa Cells Algorithms DNA, Single-Stranded Fluorescence Carbocyanines Deep Learning ErbB Receptors Fluorescent Dyes
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Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

The European respiratory journal Mar 28, 2019
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is i...
Middle Aged Deep Learning ROC Curve Humans Image Processing, Computer-Assisted Neural Networks, Computer Neoplasm Staging Female Computational Biology Retrospective Studies Mutation Reproducibility of Results Tomography, X-Ray Computed ErbB Receptors Aged Adenocarcinoma of Lung Male
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Design and synthesis of new phthalazine-based derivatives as potential EGFR inhibitors for the treatment of hepatocellular carcinoma.

Bioorganic chemistry Dec 31, 2018
Searching for new leads in the battle of cancer will never ends, we herein disclose the design and synthesis of new phthalazine derivatives and their in vitro and in vivo testing for their antiproliferative activity. Phthalazine was selected as a pri...
Carcinoma, Hepatocellular Humans Cisplatin Protein Kinase Inhibitors Cell Proliferation Molecular Docking Simulation Doxorubicin Hep G2 Cells ErbB Receptors Antineoplastic Agents Drug Screening Assays, Antitumor Phthalazines Liver Neoplasms
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