AIMC Topic: Deep Learning

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Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings.

Communications biology
Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal ident...

Sigma profiles in deep learning: towards a universal molecular descriptor.

Chemical communications (Cambridge, England)
This work showcases the remarkable ability of sigma profiles to function as molecular descriptors in deep learning. The sigma profiles of 1432 compounds are used to train convolutional neural networks that accurately correlate and predict a wide rang...

Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning.

IEEE journal of biomedical and health informatics
Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important fac...

Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods.

IEEE journal of biomedical and health informatics
Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immu...

The Application of Artificial Intelligence Technology in the Asset Management of Start-Ups in the Context of Deep Learning.

Computational intelligence and neuroscience
With the coninuous improvement and development of artificial intelligence (AI) technology, this technology has been used in the asset management of companies. To improve the asset management level of Chinese start-ups, firstly, back-propagation neura...

Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning.

PeerJ
Deep learning allows us to automatize the acquisition of large amounts of behavioural animal data with applications for fisheries and aquaculture. In this work, we have trained an image-based deep learning algorithm, the Faster R-CNN (Faster region-b...

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A ...

Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

European journal of nuclear medicine and molecular imaging
PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).