AIMC Topic: Neural Networks, Computer

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Artificial Intelligence in Oncology: Current Applications and Future Directions.

Oncology (Williston Park, N.Y.) Feb 15, 2019
Artificial Intelligence Machine Learning Neoplasms Humans Neural Networks, Computer Decision Making Medical Oncology
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Using neuroscience to develop artificial intelligence.

Science (New York, N.Y.) Feb 15, 2019
Brain Learning Deep Learning Humans Neural Networks, Computer Artificial Intelligence Neurosciences
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Artificial intelligence in medical imaging of the liver.

World journal of gastroenterology Feb 14, 2019
Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristi...
Machine Learning Algorithms Deep Learning Liver Diagnostic Imaging Liver Diseases Humans Neural Networks, Computer Artificial Intelligence
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Do Primates and Deep Artificial Neural Networks Perform Object Categorization in a Similar Manner?

The Journal of neuroscience : the official journal of the Society for Neuroscience Feb 6, 2019
Animals Models, Neurological Primates Humans Neural Networks, Computer
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[Neural network modeling of multitarget RAGE inhibitory activity].

Biomeditsinskaia khimiia Feb 1, 2019
Based on the methodology of artificial neural networks, models describing the dependence of the level of RAGE inhibitory activity on the affinity of compounds for target proteins of the RAGE-NF-kB signal pathway have been costructed. A validated data...
Receptor for Advanced Glycation End Products Molecular Docking Simulation Humans Signal Transduction Neural Networks, Computer NF-kappa B
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Identifying short disorder-to-order binding regions in disordered proteins with a deep convolutional neural network method.

Journal of bioinformatics and computational biology Feb 1, 2019
Molecular recognition features (MoRFs) are key functional regions of intrinsically disordered proteins (IDPs), which play important roles in the molecular interaction network of cells and are implicated in many serious human diseases. Identifying MoR...
Amino Acid Sequence Intrinsically Disordered Proteins Binding Sites Humans Membrane Proteins Neural Networks, Computer Deep Learning Databases, Protein Protein Binding Computational Biology
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Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins.

Journal of bioinformatics and computational biology Feb 1, 2019
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. ...
rab GTP-Binding Proteins Guanosine Triphosphate Computational Biology Amino Acid Sequence Binding Sites Databases, Protein Humans Amino Acids Neural Networks, Computer Deep Learning
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Deep convolutional network for animal sound classification and source attribution using dual audio recordings.

The Journal of the Acoustical Society of America Feb 1, 2019
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size ...
Animals Signal Processing, Computer-Assisted Sound Spectrography Vocalization, Animal Callithrix Neural Networks, Computer
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Classification of Background Parenchymal Uptake on Molecular Breast Imaging Using a Convolutional Neural Network.

JCO clinical cancer informatics Feb 1, 2019
PURPOSE: Background parenchymal uptake (BPU), which describes the level of radiotracer uptake in normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor. Our objective was to develop and vali...
Parenchymal Tissue Humans Mammography Molecular Imaging Breast Risk Factors Neural Networks, Computer Image Interpretation, Computer-Assisted Radiopharmaceuticals Breast Neoplasms Radionuclide Imaging Algorithms Risk Assessment Female
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Deep Learning for Prediction of AMD Progression: A Pilot Study.

Investigative ophthalmology & visual science Feb 1, 2019
PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning.
ROC Curve Follow-Up Studies Tomography, Optical Coherence Deep Learning Pilot Projects Wet Macular Degeneration Aged Aged, 80 and over Male Middle Aged Disease Progression Diagnosis, Computer-Assisted Neural Networks, Computer Machine Learning Female Humans
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