AIMC Topic: Neural Networks, Computer

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Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

Bioinformatics (Oxford, England)
MOTIVATION: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions b...

A critical review on the applications of artificial neural networks in winemaking technology.

Critical reviews in food science and nutrition
Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance s...

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

SAR and QSAR in environmental research
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to ...

Unsupervised modulation filter learning for noise-robust speech recognition.

The Journal of the Acoustical Society of America
The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data-driven unsupervise...

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mec...

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

Journal of biomechanical engineering
Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are ...

Neuro-symbolic representation learning on biological knowledge graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to g...

HLA class I binding prediction via convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex ...

Deep learning in bioinformatics.

Briefings in bioinformatics
In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art perf...

[The application of artificial neural network on the assessment of lexical tone production of pediatric cochlear implant users].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
The present study was carried out to explore the tone production ability of the Mandarin-speaking children with cochlear implants (CI) by using an artificial neural network model and to examine the potential contributing factors underlining their to...