AIMC Topic: Neural Networks, Computer

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Sequential Learning in the Dense Associative Memory.

Neural computation
Sequential learning involves learning tasks in a sequence and proves challenging for most neural networks. Biological neural networks regularly succeed at the sequential learning challenge and are even capable of transferring knowledge both forward a...

A technological convergence in hepatobiliary oncology: Evolving roles of smart surgical systems.

Bioscience trends
Cancer remains a major threat to human health, with the incidence of hepatobiliary tumors consistently high. Treatment methods for hepatobiliary tumors include surgical intervention, ablation, embolization, and pharmacological treatments, with surger...

HS-GC-IMS couples with convolutional neural network for Burkholderia gladioli pv. Cocovenenans detection in Auricularia Auricula.

Food chemistry
The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for food poisoning. Combining Headspace-Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) with c...

DeepPhosPPI: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein-protein interactions.

Briefings in bioinformatics
Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and c...

DeepMobilome: predicting mobile genetic elements using sequencing reads of microbiomes.

Briefings in bioinformatics
MOTIVATION: Mobile genetic elements (MGEs) play an important role in facilitating the acquisition of antibiotic resistance genes (ARGs) within microbial communities, significantly impacting the evolution of antibiotic resistance. Understanding the me...

Compositional analysis of alternative protein blends using near and mid-infrared spectroscopy coupled with conventional and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The non-invasive real-time analysis of the composition of alternative, plant-based protein sources is important to control high moisture extrusion processes and ensure the quality and texture of the final extrudates used in the elaboration of meat an...

Implementation of convolutional neural networks for microbial colony recognition.

Microbiology spectrum
Initial classification of microorganisms based on visual identification of colonies remains challenging for skilled microbiologists and is influenced by the proficiency and subjective interpretation of professionals. To overcome these challenges, we ...

DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy.

Biology of the cell
Deep learning methods using convolutional neural networks are very effective for automatic image segmentation tasks with no exception for cellular electron micrographs. However, the lack of dedicated easy-to-use tools largely reduces the widespread u...

A Novel Deep Siamese Convolution Network for Detecting Fentanyl Analogs From Mass Spectra.

Journal of mass spectrometry : JMS
Mortality rates have risen dramatically in recent years due to the misuse of fentanyl and its analogs. Due to the easy synthesis and rapid emergence of various fentanyl analogs, an accurate detection model is particularly desirable. The existing clas...

A predictive quality assurance model for patient-specific gamma passing rate of hyperarc-based stereotactic radiotherapy and radiosurgery of brain metastases.

Journal of applied clinical medical physics
OBJECTIVE: Measurement-based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same-day stereotactic treatments limit the time to do measurement-bas...