AIMC Topic: Neural Networks, Computer

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Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.

Chemical research in toxicology
Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated ...

Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...

Computational modeling of interventions for developmental disorders.

Psychological review
We evaluate the potential of connectionist models of developmental disorders to offer insights into the efficacy of interventions. Based on a range of computational simulation results, we assess factors that influence the effectiveness of interventio...

Synchronization of impulsive coupled complex-valued neural networks with delay: The matrix measure method.

Neural networks : the official journal of the International Neural Network Society
In this paper, the exponential synchronization of the impulsive coupled delayed complex-valued neural networks (CVNNs) is studied. Without constructing the Lyapunov function, a novel approach based on the matrix measure and extended Halanay inequalit...

Estimation of radon flux spatial distribution in Rize, Turkey by the artificial neural networks method.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
In this study, average radon flux distribution in the Rize province (Turkey) was estimated by the artificial neural networks (ANN) method. For this purpose, terrestrial gamma dose rate (TGDR), which is defined as an important proxy in determining rad...

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the auto...

A deep survival analysis method based on ranking.

Artificial intelligence in medicine
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineer...

Rapid energy expenditure estimation for ankle assisted and inclined loaded walking.

Journal of neuroengineering and rehabilitation
BACKGROUND: Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from w...

Deep convolutional neural networks for mammography: advances, challenges and applications.

BMC bioinformatics
BACKGROUND: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep...

[Artificial Intelligence in Endoscopy: Deep Neural Nets for Endoscopic Computer Vision - Methods & Perspectives].

Zeitschrift fur Gastroenterologie
Artificial neural networks, as a specific approach towards artificial intelligence (AI), can open up a variety of new perspectives for endoscopy, such as automated lesion detection and the precise prediction of a lesion's histology by its endoscopic ...