AIMC Topic: Machine Learning

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Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex.

The Journal of neuroscience : the official journal of the Society for Neuroscience Oct 6, 2017
Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear "mixed...
Prefrontal Cortex Cognition Cluster Analysis Animals Male Models, Neurological Neurons Macaca mulatta Neural Networks, Computer Machine Learning Algorithms Computer Simulation Learning Female
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Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach.

Heart & lung : the journal of critical care Oct 6, 2017
BACKGROUND: Studies had shown that mortality due to ST-elevation myocardial infarction (STEMI) is higher in women compared with men. The purpose of this study is to develop and validate prediction models for all-cause in-hospital mortality in women a...
United States ST Elevation Myocardial Infarction Cause of Death Risk Assessment Hospital Mortality Humans Machine Learning Prognosis Retrospective Studies Female Aged
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Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.

AJNR. American journal of neuroradiology Oct 5, 2017
Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challeng...
Humans Machine Learning Brain Neoplasms Image Interpretation, Computer-Assisted Neuroimaging
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SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

Neural networks : the official journal of the International Neural Network Society Oct 5, 2017
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in ...
Neural Networks, Computer Machine Learning Neurons Algorithms Nonlinear Dynamics Synapses Computational Biology Cluster Analysis
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Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.

International journal of medical informatics Oct 5, 2017
BACKGROUND: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By ...
ROC Curve Bayes Theorem Outcome Assessment, Health Care Heart Diseases Severity of Illness Index Adolescent Hospital Mortality Humans Male Machine Learning Middle Aged Databases, Factual Aged, 80 and over Intensive Care Units Young Adult Female Adult Aged
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Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

IEEE transactions on medical imaging Oct 5, 2017
Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint...
Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Machine Learning Prostate Algorithms Databases, Factual Female Male Head
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Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Journal of chemical information and modeling Oct 5, 2017
Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanopart...
Models, Chemical Silver Electron Transport Machine Learning Nanoparticles
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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

PloS one Oct 5, 2017
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism's development, disease, and evolution. The building blocks of this complex molecular machinery are an organism's genomic...
Support Vector Machine Machine Learning Transcription Factors Nucleotides Binding Sites
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Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PloS one Oct 5, 2017
In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiologic...
Disease Susceptibility Humans Precision Medicine Medicine, Ayurvedic Machine Learning Surveys and Questionnaires India Phenotype
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Phenotype Prediction from Metagenomic Data Using Clustering and Assembly with Multiple Instance Learning (CAMIL).

IEEE/ACM transactions on computational biology and bioinformatics Oct 4, 2017
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper, we demonstrate a novel, efficient, ...
Cluster Analysis Metagenomics Metagenome Humans Machine Learning Phenotype
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