AIMC Topic: Neural Networks, Computer

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Polarization-driven camouflaged object segmentation via gated fusion.

Applied optics
Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity fe...

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, di...

ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...

R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting.

Briefings in bioinformatics
RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was use...

DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins.

Briefings in bioinformatics
Protein S-sulfinylation is an important posttranslational modification that regulates a variety of cell and protein functions. This modification has been linked to signal transduction, redox homeostasis and neuronal transmission in studies. Therefore...

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.

Briefings in bioinformatics
Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of po...

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Briefings in bioinformatics
Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemica...

multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.

Briefings in bioinformatics
MOTIVATION: Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods focused on integrating multisource data about drugs and proteins for predicting drug-target interaction...

GNN-SubNet: disease subnetwork detection with explainable graph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: The tremendous success of graphical neural networks (GNNs) already had a major impact on systems biology research. For example, GNNs are currently being used for drug target recognition in protein-drug interaction networks, as well as for...

Tunable grating surfaces with high diffractive efficiency optimized by deep neural networks.

Optics letters
High diffractive efficiency gratings, as a core component in optics, can engineer light transport and separation. This Letter predicts a grating surface with high diffractive efficiency within the visible light wave band with the aid of deep neural n...