AIMC Topic:
Databases, Factual

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HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics Aug 8, 2019
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...
Unsupervised Machine Learning Neuroblastoma Deep Learning Computational Biology Models, Statistical Humans Databases, Factual Transcriptome
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Improving the Rate of Translation of Tissue Engineering Products.

Advanced healthcare materials Aug 6, 2019
Over 100 000 research articles and 9000 patents have been published on tissue engineering (TE) in the past 20 years. Yet, very few TE products have made their way to the market during the same period. Experts have proposed a variety of strategies to ...
Humans Machine Learning Translational Research, Biomedical Regenerative Medicine Tissue Engineering Organ Culture Techniques Algorithms Databases, Factual Organ Transplantation Genetic Therapy Cell- and Tissue-Based Therapy
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Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BMC bioinformatics Aug 6, 2019
BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the ...
Drug Interactions Models, Theoretical Support Vector Machine Area Under Curve Humans Neural Networks, Computer Databases, Factual Deep Learning
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Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Journal of shoulder and elbow surgery Aug 3, 2019
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.
Blood Transfusion Aged Smoking ROC Curve Forecasting Male Reoperation Operative Time Postoperative Complications Supervised Machine Learning Body Mass Index Databases, Factual Serum Albumin Comorbidity Predictive Value of Tests Middle Aged Age Factors Hematocrit Female Arthroplasty, Replacement, Shoulder Length of Stay Patient Readmission Humans
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Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports.

IEEE journal of biomedical and health informatics Aug 2, 2019
Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, ...
Humans Algorithms Databases, Factual Drug-Related Side Effects and Adverse Reactions Decision Support Systems, Clinical Adverse Drug Reaction Reporting Systems Supervised Machine Learning
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Low-rank analysis-synthesis dictionary learning with adaptively ordinal locality.

Neural networks : the official journal of the International Neural Network Society Aug 2, 2019
Analysis dictionary learning (ADL) has been successfully applied to a variety of learning systems. However, the ordinal locality of analysis dictionary has rarely been explored in constructing discriminative terms. In this paper, a discriminative low...
Machine Learning Humans Algorithms Databases, Factual
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Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram.

Computer methods and programs in biomedicine Jul 30, 2019
BACKGROUND AND OBJECTIVE: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal.
Humans Electrocardiography Databases, Factual Sleep Apnea Syndromes Deep Learning Automation
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Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal of biomedical informatics Jul 30, 2019
AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical softwa...
Supervised Machine Learning Coronary Disease Prospective Studies Risk Factors Humans Models, Statistical Logistic Models Neural Networks, Computer Computational Biology Decision Trees Support Vector Machine Algorithms Area Under Curve Longitudinal Studies Databases, Factual
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A deep convolutional neural network for the estimation of gas chromatographic retention indices.

Journal of chromatography. A Jul 29, 2019
A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking ...
Neural Networks, Computer Databases, Factual Gas Chromatography-Mass Spectrometry Regression Analysis Chromatography, Gas
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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics Jul 29, 2019
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...
Neoplasms Antineoplastic Agents Deep Learning Cell Line, Tumor Genomics Regression Analysis Neural Networks, Computer Humans Phenotype Inhibitory Concentration 50 Databases, Factual Organ Specificity
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