AIMC Topic: Neural Networks, Computer

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AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation.

Journal of digital imaging Feb 1, 2019
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low...
Humans Neural Networks, Computer Algorithms Deep Learning Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Brain Reproducibility of Results Brain Neoplasms
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[A DenseNet-based diagnosis algorithm for automated diagnosis using clinical ECG data].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University Jan 30, 2019
OBJECTIVE: To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical diagnosis.
Arrhythmias, Cardiac Databases as Topic Algorithms Sensitivity and Specificity Electrocardiography Humans Neural Networks, Computer
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Lyapunov functional for virus infection model with diffusion and state-dependent delays.

Mathematical biosciences and engineering : MBE Jan 29, 2019
In this paper, a virus dynamics model with diffusion, state-dependent delays and a general nonlinear functional response is investigated. At first, the dynamical system is constructed on a nonlinear metric space. Then the stability of the interior eq...
Cell Survival Models, Biological Diffusion Nonlinear Dynamics Algorithms Humans Virus Diseases Computer Simulation Neural Networks, Computer Time Factors
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A new ensemble residual convolutional neural network for remaining useful life estimation.

Mathematical biosciences and engineering : MBE Jan 28, 2019
Remaining useful life (RUL) estimation is one of the most important component in prognostic health management (PHM) system in modern industry. It defined as the length from the current time to the end of the useful life. With the rapid development of...
Prognosis Learning Deep Learning Algorithms Computer Simulation Neural Networks, Computer Internet Linear Models Time Factors Longevity Support Vector Machine Fuzzy Logic
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An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.

Briefings in bioinformatics Jan 18, 2019
Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used i...
Humans Computational Biology Support Vector Machine Bayes Theorem Bacterial Proteins Models, Biological Virulence Algorithms Databases, Factual Markov Chains Computer Simulation Neural Networks, Computer Machine Learning Bacterial Secretion Systems Host Microbial Interactions
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LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England) Jan 15, 2019
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...
Ligands Protein Conformation Binding Sites Computational Biology Proteins Neural Networks, Computer Software Drug Discovery Protein Binding
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Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.

Bioinformatics (Oxford, England) Jan 15, 2019
MOTIVATION: In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic...
Proteins Protein Interaction Mapping Neural Networks, Computer Drug Discovery Deep Learning
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Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Chemical communications (Cambridge, England) Jan 10, 2019
Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation...
Stochastic Processes Leukemia, Myelogenous, Chronic, BCR-ABL Positive Metabolomics Drug Resistance, Neoplasm K562 Cells Logistic Models Mass Spectrometry Humans Neural Networks, Computer Machine Learning Phenotype Single-Cell Analysis
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Can Deep Learning Model Perceptual Learning?

The Journal of neuroscience : the official journal of the Society for Neuroscience Jan 9, 2019
Neural Networks, Computer Deep Learning Visual Perception Spatial Learning
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Computers turn neural signals into speech.

Science (New York, N.Y.) Jan 4, 2019
Brain-Computer Interfaces Brain Neoplasms Speech Humans Epilepsy Neural Networks, Computer Computers
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