AIMC Topic: Neural Networks, Computer

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A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

Journal of neuroscience methods
BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). Hippocampal atrophy can be evaluated in terms of hippocampal volumes and shapes using ...

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds.

Molecules (Basel, Switzerland)
P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties...

AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks.

NeuroImage. Clinical
Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement betw...

PSACNN: Pulse sequence adaptive fast whole brain segmentation.

NeuroImage
With the advent of convolutional neural networks (CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervi...

THPep: A machine learning-based approach for predicting tumor homing peptides.

Computational biology and chemistry
In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agent...

Local online learning in recurrent networks with random feedback.

eLife
Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsi...

Categorization of free-text drug orders using character-level recurrent neural networks.

International journal of medical informatics
BACKGROUND AND PURPOSE: Manual annotation and categorization of non-standardized text ("free-text") of drug orders entered into electronic health records is a labor-intensive task. However, standardization is required for drug order analyses and has ...

HypernasalityNet: Deep recurrent neural network for automatic hypernasality detection.

International journal of medical informatics
BACKGROUND: Cleft palate patients have inability to produce adequate velopharyngeal closure, which results in hypernasal speech. In clinic, hypernasal speech is assessed through subject assessment by speech language pathologists. Automatic hypernasal...

Effect of Depth and Width on Local Minima in Deep Learning.

Neural computation
In this paper, we analyze the effects of depth and width on the quality of local minima, without strong overparameterization and simplification assumptions in the literature. Without any simplification assumption, for deep nonlinear neural networks w...

Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN).

Computational intelligence and neuroscience
Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precauti...