AIMC Topic: Neural Networks, Computer

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Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Optics letters
Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-b...

Image-free multi-character recognition.

Optics letters
The recently developed image-free sensing technique decouples semantic information directly from compressed measurements without image reconstruction, which maintains the advantages of both the light hardware and software. However, the existing attem...

GSEnet: feature extraction of gene expression data and its application to Leukemia classification.

Mathematical biosciences and engineering : MBE
Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, ...

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Optics express
Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive component...

Measuring laser beams with a neural network.

Applied optics
A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii, and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with experimental la...

Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.

Briefings in bioinformatics
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent y...

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Briefings in bioinformatics
Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an ess...

Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction.

Briefings in bioinformatics
Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a ...

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Briefings in bioinformatics
Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. H...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Briefings in bioinformatics
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secr...