AIMC Topic: Neural Networks, Computer

Clear Filters Showing 9941 to 9950 of 31376 articles

Memory-efficient Transformer-based network model for Traveling Salesman Problem.

Neural networks : the official journal of the International Neural Network Society
Combinatorial optimization problems such as Traveling Salesman Problem (TSP) have a wide range of real-world applications in transportation, logistics, manufacturing. It has always been a difficult problem to solve large-scale TSP problems quickly be...

Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

The journal of physical chemistry letters
Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and ph...

Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.)
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

Pharmaceutical research
PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thiofl...

Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.

Sensors (Basel, Switzerland)
Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their ...

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.

Genes
Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been ...

Deep reinforcement learning-based pairwise DNA sequence alignment method compatible with embedded edge devices.

Scientific reports
Sequence alignment is an essential component of bioinformatics, for identifying regions of similarity that may indicate functional, structural, or evolutionary relationships between the sequences. Genome-based diagnostics relying on DNA sequencing ha...

3D ECG display with deep learning approach for identification of cardiac abnormalities from a variable number of leads.

Physiological measurement
The objective of this study is to explore new imaging techniques with the use of the deep learning method for the identification of cardiac abnormalities present in electrocardiogram (ECG) signals with 2, 3, 4, 6 and 12-lead in the framework of the P...

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Journal of biomedical optics
SIGNIFICANCE: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP...

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.

PloS one
Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses r...