AIMC Topic: Neural Networks, Computer

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Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles.

IEEE/ACM transactions on computational biology and bioinformatics
Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the i...

Sequential method for fast neural population activity reconstruction in the cortex from incomplete noisy measurements.

Computers in biology and medicine
During recent years there has been a growing interest in stochastic dynamic neural fields employed for modeling and predictions in biomedical and technical systems. In this paper, given some incomplete noisy data available from sensors, we propose an...

Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images.

Computers in biology and medicine
UNLABELLED: The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challengi...

Learning Meta-Distance for Sequences by Learning a Ground Metric via Virtual Sequence Regression.

IEEE transactions on pattern analysis and machine intelligence
Distance between sequences is structural by nature because it needs to establish the temporal alignments among the temporally correlated vectors in sequences with varying lengths. Generally, distances for sequences heavily depend on the ground metric...

A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
The rapid development of deep neural networks (DNNs) in recent years can be attributed to the various techniques that address gradient explosion and vanishing. In order to understand the principle behind these techniques and develop new methods, plen...

Event-Based Vision: A Survey.

IEEE transactions on pattern analysis and machine intelligence
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location an...

Scalable and Practical Natural Gradient for Large-Scale Deep Learning.

IEEE transactions on pattern analysis and machine intelligence
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previous approaches attempt to address this problem by varying the learning...

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset.

Sensors (Basel, Switzerland)
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To expl...

Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets.

Molecules (Basel, Switzerland)
The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form ...

BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters.

Molecules (Basel, Switzerland)
An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC)...