AIMC Topic: Neural Networks, Computer

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Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Environmental pollution (Barking, Essex : 1987)
Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to i...

Justifying diagnosis decisions by deep neural networks.

Journal of biomedical informatics
An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition...

ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins.

BMC bioinformatics
BACKGROUND: Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. It is also the major process to e...

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

IEEE transactions on medical imaging
Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and a...

CADOES: An interactive machine-learning approach for sex estimation with the pelvis.

Forensic science international
The pelvis is consistently regarded as the most sexually dimorphic region of the human skeleton, and methods for sex estimation with the pelvic bones are usually very accurate. In this investigation, population-specific osteometric models for the ass...

A practical guide to intelligent image-activated cell sorting.

Nature protocols
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell s...

Deep Reinforcement Learning for Multiparameter Optimization in Drug Design.

Journal of chemical information and modeling
In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods f...

Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.

Sensors (Basel, Switzerland)
A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery...

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases.

Sensors (Basel, Switzerland)
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on ho...

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters.

Biomedical engineering online
BACKGROUND: Hemodialysis mainly relies on the "artificial kidney," which plays a very important role in temporarily or permanently substituting for the kidney to carry out the exchange of waste and discharge of water. Nevertheless, a previous study o...