AIMC Topic: Neural Networks, Computer

Clear Filters Showing 29811 to 29820 of 31376 articles

TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires develo...

Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks.

Mathematical biosciences and engineering : MBE
Despite the increasing use of technology, handwriting has remained to date as an efficient means of communication. Certainly, handwriting is a critical motor skill for childrens cognitive development and academic success. This article presents a new ...

Artificial neural networks for the inverse design of nanoparticles with preferential nano-bio behaviors.

The Journal of chemical physics
Safe and efficient use of ultrasmall nanoparticles (NPs) in biomedicine requires numerous independent conditions to be met, including colloidal stability, selectivity for proteins and membranes, binding specificity, and low affinity for plasma protei...

Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient.

Optics express
Different techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coefficient (c) was developed to estimate chlorophyll-a con...

Classification of cell morphology with quantitative phase microscopy and machine learning.

Optics express
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifie...

Applications of a neural network to detect the percolating transitions in a system with variable radius of defects.

Chaos (Woodbury, N.Y.)
We systematically study the percolation phase transition at the change of concentration of the chaotic defects (pores) in an extended system where the disordered defects additionally have a variable random radius, using the methods of a neural networ...

Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions.

Journal of biomedical optics
SIGNIFICANCE: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as...

HONEM: Learning Embedding for Higher Order Networks.

Big data
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning meth...

Comparison of data analytics strategies in computer vision systems to predict pig body composition traits from 3D images.

Journal of animal science
Computer vision systems (CVS) have been shown to be a powerful tool for the measurement of live pig body weight (BW) with no animal stress. With advances in precision farming, it is now possible to evaluate the growth performance of individual pigs m...

Efficient prediction of drug-drug interaction using deep learning models.

IET systems biology
A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting th...