AIMC Topic: Mutation

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Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Scientific reports Dec 30, 2019
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a di...
Humans Neoplasm Grading Mutation Promoter Regions, Genetic Isocitrate Dehydrogenase Telomerase Male Image Processing, Computer-Assisted Neural Networks, Computer Glioma Female Magnetic Resonance Imaging Reproducibility of Results Neoplasm Staging Biomarkers, Tumor
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Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science Dec 6, 2019
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...
Protein Binding Computational Biology Support Vector Machine Receptors, G-Protein-Coupled Mutation Machine Learning Software Algorithms Animals Protein Interaction Mapping Protein Structure, Secondary Protein Multimerization Allosteric Site Molecular Dynamics Simulation Ligands Fluorescence Resonance Energy Transfer Crystallography, X-Ray Humans Neural Networks, Computer
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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.
ErbB Receptors Decision Making, Computer-Assisted Male Middle Aged Lung Neoplasms Mutation Humans Neural Networks, Computer Female Aged
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Evaluation of colorectal cancer subtypes and cell lines using deep learning.

Life science alliance Dec 2, 2019
Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cel...
Humans Machine Learning Colorectal Neoplasms Sequence Analysis, DNA DNA Copy Number Variations Neoplasm Proteins Biomarkers, Tumor Mutation Gene Expression Profiling Models, Biological Deep Learning Cell Line, Tumor
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Ranking of non-coding pathogenic variants and putative essential regions of the human genome.

Nature communications Nov 20, 2019
A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. H...
INDEL Mutation Humans Computer Simulation Enhancer Elements, Genetic Genetic Variation DNA Genes, Reporter Genome, Human Chromatin Gene Expression Nucleic Acid Conformation Mutation Introns Supervised Machine Learning
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Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.

Japanese journal of radiology Nov 18, 2019
PURPOSE: To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).
Humans Machine Learning Brain Retrospective Studies Female Middle Aged Glioma Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Predictive Value of Tests Reproducibility of Results Isocitrate Dehydrogenase Mutation Adult Brain Neoplasms Male
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CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

Scientific reports Nov 15, 2019
With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alteration...
Genetic Variation Linear Models Genomics Mutation Genetic Testing Humans Workflow Neural Networks, Computer Machine Learning Algorithms Databases, Factual Reproducibility of Results Neoplasms
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Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational tools.

Cancer reports (Hoboken, N.J.) Nov 11, 2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the a...
Microtubules Tumor Suppressor Protein p53 Tumor Suppressor Proteins DNA Modification Methylases DNA Mutational Analysis DNA Repair Enzymes Drug Resistance, Neoplasm Humans Prognosis Computational Biology Machine Learning Glioblastoma Tumor Microenvironment Cancer Vaccines Brain Mutation Brain Neoplasms Models, Genetic Precision Medicine Genetic Heterogeneity Immune Checkpoint Inhibitors Antineoplastic Combined Chemotherapy Protocols Chemoradiotherapy, Adjuvant Clonal Evolution
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SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

Science advances Nov 6, 2019
We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-indu...
Deep Learning CRISPR-Cas Systems Mutation Internet Gene Editing RNA, Guide, CRISPR-Cas Systems CRISPR-Associated Protein 9 Humans Reproducibility of Results
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DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Frontiers in immunology Nov 1, 2019
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In th...
Humans Epitopes, T-Lymphocyte Neoplasms CD8-Positive T-Lymphocytes Peptides Mutation Deep Learning HLA Antigens Antigens, Neoplasm
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