AIMC Topic: Mutation

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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...
Algorithms Tomography, X-Ray Computed Female Reproducibility of Results Male Humans Neural Networks, Computer Neoplasm Staging Mutation ErbB Receptors Data Curation Adenocarcinoma of Lung Deep Learning ROC Curve Image Processing, Computer-Assisted Computational Biology
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Selene: a PyTorch-based deep learning library for sequence data.

Nature methods Mar 28, 2019
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...
Humans Neural Networks, Computer Software Algorithms Models, Statistical Mutagenesis Area Under Curve Normal Distribution Mutation Deep Learning Gene Library Alzheimer Disease Programming Languages Genomics Computational Biology Sequence Analysis, DNA
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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...
Aged Male Middle Aged ErbB Receptors Deep Learning Adenocarcinoma of Lung Computational Biology Retrospective Studies Neoplasm Staging Humans Tomography, X-Ray Computed Mutation Female ROC Curve Neural Networks, Computer Reproducibility of Results Image Processing, Computer-Assisted
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LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BMC cancer Mar 22, 2019
BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction...
Models, Biological Survival Analysis Datasets as Topic Adenocarcinoma of Lung Survival Rate Biomarkers, Tumor ROC Curve Lung Neoplasms Support Vector Machine Humans Mutation Feasibility Studies Precision Medicine Genomics Prognosis Computational Biology
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Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

Molecular and cellular biochemistry Mar 21, 2019
This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectr...
Infant Adolescent Child, Preschool Child Mutation Tandem Mass Spectrometry Glycosaminoglycans Iduronidase Chromatography, Liquid Machine Learning Acetylglucosaminidase Humans Diagnosis, Differential Female Hydrolases Adult Mucopolysaccharidoses Male Chondroitinsulfatases
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Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

IEEE/ACM transactions on computational biology and bioinformatics Mar 18, 2019
As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an ...
Gene Expression Profiling Transcriptome Lung Neoplasms Cluster Analysis Mutation Adenocarcinoma of Lung Humans Neural Networks, Computer Algorithms Biomarkers, Tumor
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Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas.

Clinical radiology Mar 5, 2019
This paper describes state-of-the-art methods for molecular biomarker prediction utilising magnetic resonance imaging. This review paper covers both classical machine learning approaches and deep learning approaches to identifying the predictive feat...
Mutation Radiobiology Deep Learning Genomics Glioma Magnetic Resonance Imaging Humans Biomarkers, Tumor Brain Neoplasms
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Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications Mar 4, 2019
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...
Humans Neural Networks, Computer Sequence Analysis, DNA Mutation DNA Mutational Analysis Sequence Alignment Genes, Neoplasm Machine Learning Neoplasms Computational Biology Databases, Genetic Exome Diploidy
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Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Scientific reports Feb 26, 2019
Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window ar...
DNA Breaks, Double-Stranded DNA Repair RNA, Guide, CRISPR-Cas Systems Oligodeoxyribonucleotides Machine Learning CRISPR-Cas Systems Mice Animals Mice, Inbred C57BL Mutation Gene Editing
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Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BMC medical genomics Jan 31, 2019
BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer ...
Benzothiazoles Naphthyridines Humans Cell Line, Tumor Antineoplastic Agents Genomics Transcriptome Mutation Deep Learning Docetaxel
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