AIMC Topic: Machine Learning

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Non-Gaussian Methods for Causal Structure Learning.

Prevention science : the official journal of the Society for Prevention Research Apr 1, 2019
Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Neverth...
Causality Empirical Research Machine Learning
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MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery.

Annals of surgery Apr 1, 2019
OBJECTIVE: To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data.
Humans Risk Assessment Postoperative Complications Preoperative Period Machine Learning Algorithms
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TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis.

Mathematical biosciences and engineering : MBE Mar 26, 2019
The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way f...
Cloud Computing Computers False Positive Reactions Bayes Theorem Support Vector Machine Decision Making Machine Learning Computer Security Algorithms Area Under Curve
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Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.

Journal of speech, language, and hearing research : JSLHR Mar 25, 2019
Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)-based appr...
Speech Perception Machine Learning Evoked Potentials, Auditory Humans
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Machine Learning in Drug Discovery.

Journal of chemical information and modeling Mar 25, 2019
Drug Discovery Machine Learning
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A new deep learning model for assisted diagnosis on electrocardiogram.

Mathematical biosciences and engineering : MBE Mar 22, 2019
In order to enhance the accuracy of computer aided electrocardiogram analysis, we propose a deep learning model called CBRNN to assist diagnosis on electrocardiogram for clinical medical service. It combines two sub networks which are convolutional n...
Cardiology Medical Errors Medical Informatics Models, Cardiovascular Neural Networks, Computer Electrocardiography Diagnosis, Computer-Assisted Signal Processing, Computer-Assisted Machine Learning Humans Deep Learning Algorithms Skin
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Identification of hormone binding proteins based on machine learning methods.

Mathematical biosciences and engineering : MBE Mar 22, 2019
The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequis...
Machine Learning Software Computer Simulation Reproducibility of Results Algorithms Humans Databases, Protein Amino Acids Computational Biology Analysis of Variance Hormones Animals Support Vector Machine Carrier Proteins Peptides
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Adversarial attacks on medical machine learning.

Science (New York, N.Y.) Mar 22, 2019
Humans Fraud Insurance Claim Review Machine Learning
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Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Briefings in bioinformatics Mar 22, 2019
Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repo...
Humans Machine Learning Biomarkers, Tumor Gene Expression Profiling Receptor, ErbB-2 Receptors, Progesterone Receptors, Estrogen Computational Biology Prognosis Female Breast Neoplasms Algorithms Phenotype
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High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

Metallomics : integrated biometal science Mar 20, 2019
One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper. Recently, our group demonstrated that previousl...
Microbial Sensitivity Tests Staphylococcus aureus Bayes Theorem Machine Learning Anti-Bacterial Agents Small Molecule Libraries Copper High-Throughput Screening Assays
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