AIMC Topic:
Databases, Factual

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Reporting and connecting cell type names and gating definitions through ontologies.

BMC bioinformatics Apr 25, 2019
BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a p...
Biological Ontologies Humans Immune System Databases, Factual Protein Subunits Proteins
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The cell line ontology-based representation, integration and analysis of cell lines used in China.

BMC bioinformatics Apr 25, 2019
BACKGROUND: The Chinese National Infrastructure of Cell Line stores and distributes cell lines for biomedical research in China. This study aims to represent and integrate the information of NICR cell lines into the community-based Cell Line Ontology...
Biological Ontologies Humans Databases, Factual Biomedical Research China User-Computer Interface Cell Line
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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Computers in biology and medicine Apr 24, 2019
OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for...
Sepsis Adolescent Vital Signs Middle Aged Severity of Illness Index Diagnosis, Computer-Assisted Aged Adult Male Databases, Factual Intensive Care Units Electronic Health Records Humans Female Machine Learning
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Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

IEEE journal of biomedical and health informatics Apr 22, 2019
Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities t...
Humans Algorithms Databases, Factual Neuroimaging Deep Learning Image Processing, Computer-Assisted Mammography
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Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization.

Scientific reports Apr 18, 2019
Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural net...
Deep Learning Image Processing, Computer-Assisted Support Vector Machine Machine Learning Algorithms Databases, Factual X-Rays Radiography Tuberculosis Thorax Humans Neural Networks, Computer
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PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison.

BMC bioinformatics Apr 18, 2019
BACKGROUND: Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant - drug - adverse ev...
Humans Databases, Factual Electronic Health Records Tissue Banks Pharmacogenetics Knowledge Bases Data Mining
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Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

The oncologist Apr 17, 2019
BACKGROUND: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained dee...
Retrospective Studies Tomography, X-Ray Computed Female Neural Networks, Computer Humans Deep Learning Algorithms ROC Curve Databases, Factual Lung Neoplasms Male Radiographic Image Interpretation, Computer-Assisted Middle Aged Lung
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Photomontage detection using steganography technique based on a neural network.

Neural networks : the official journal of the International Neural Network Society Apr 17, 2019
This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN m...
Databases, Factual Reproducibility of Results Pattern Recognition, Automated Humans Neural Networks, Computer Photography
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Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net.

IEEE transactions on medical imaging Apr 16, 2019
The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) dr...
Databases, Factual Image Processing, Computer-Assisted Humans Tomography, X-Ray Computed Pancreas Algorithms Deep Learning
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A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

Osteoarthritis and cartilage Apr 16, 2019
OBJECTIVE: Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progressi...
Menisci, Tibial Congresses as Topic National Institutes of Health (U.S.) Biological Variation, Population Osteoarthritis, Knee Collagen Type II Cartilage, Articular Severity of Illness Index United States Pain Measurement Databases, Factual Biomarkers Female Middle Aged Disease Progression Aged Male Humans Machine Learning
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