AIMC Topic: Neural Networks, Computer

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Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.

Bioinformatics (Oxford, England)
MOTIVATION: The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The id...

Deep Learning for Cardiovascular Imaging: A Review.

JAMA cardiology
IMPORTANCE: Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, ...

AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection.

Journal of integrative neuroscience
BACKGROUND: In recent years, road traffic safety has become a prominent issue due to the worldwide proliferation of vehicles on roads. The challenge of driver fatigue detection involves balancing the efficiency and accuracy of the detection process. ...

Noisy image segmentation based on synchronous dynamics of coupled photonic spiking neurons.

Optics express
The collective dynamics in neural networks is essential for information processing and has attracted much interest on the application in artificial intelligence. Synchronization is one of the most dominant phenomenon in the collective dynamics of neu...

Explanation Relevance Index - A New Way of Assessing the Quality of Explanations Based on Learned Features.

Studies in health technology and informatics
The XAI methods began to emerge as a response for the black-box methods used to make decisions that could not be explained, even if checked by humans they were correct. This created the need for a better evaluation for these methods. In this paper we...

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies.

Neural computation
Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learni...

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.

Molecular biology and evolution
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involve...

Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks.

Molecular biology and evolution
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which use...

Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based pr...

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and lat...