AIMC Topic: Neural Networks, Computer

Clear Filters Showing 29491 to 29500 of 31376 articles

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.

Nucleic acids research
Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this pap...

Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm.

Mathematical biosciences and engineering : MBE
Fetal weight is an important index to judge fetal development and ensure the safety of pregnant women. However, fetal weight cannot be directly measured. This study proposed a prediction model of fetal weight based on genetic algorithm to optimize ba...

Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images.

Applied optics
Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extrac...

TERL: classification of transposable elements by convolutional neural networks.

Briefings in bioinformatics
Transposable elements (TEs) are the most represented sequences occurring in eukaryotic genomes. Few methods provide the classification of these sequences into deeper levels, such as superfamily level, which could provide useful and detailed informati...

DeepAtomicCharge: a new graph convolutional network-based architecture for accurate prediction of atomic charges.

Briefings in bioinformatics
Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction ...

Interpretation of deep learning in genomics and epigenomics.

Briefings in bioinformatics
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies tow...

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm.

Briefings in bioinformatics
As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artifici...

RNA-binding protein recognition based on multi-view deep feature and multi-label learning.

Briefings in bioinformatics
RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict ...

DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences.

Briefings in bioinformatics
Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology...

Using deep neural networks and biological subwords to detect protein S-sulfenylation sites.

Briefings in bioinformatics
Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling t...