AIMC Topic: Deep Learning

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ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.

Computational biology and chemistry Jan 7, 2025
The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying re...
Computer Simulation Deep Learning Escherichia coli Anti-Bacterial Agents Drug Resistance, Bacterial
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Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Neural networks : the official journal of the International Neural Network Society Jan 7, 2025
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. Th...
Deep Learning Electroencephalography Brain-Computer Interfaces Evoked Potentials Attention Algorithms Brain Humans Neural Networks, Computer
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Graph neural networks in histopathology: Emerging trends and future directions.

Medical image analysis Jan 7, 2025
Histopathological analysis of whole slide images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fail to capture the intricate spatial dependencies inherent i...
Humans Neural Networks, Computer Image Interpretation, Computer-Assisted Graph Neural Networks Deep Learning
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Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans.

Computers in biology and medicine Jan 7, 2025
PURPOSE: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segm...
Adipose Tissue Image Processing, Computer-Assisted Muscle, Skeletal Aged Adult Middle Aged Deep Learning Male Tomography, X-Ray Computed Female Humans Algorithms
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Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.

International journal of computer assisted radiology and surgery Jan 7, 2025
PURPOSE: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We pr...
Deep Learning Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted Humans Tomography, X-Ray Computed Neural Networks, Computer Lung
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High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Food research international (Ottawa, Ont.) Jan 7, 2025
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...
Hyperspectral Imaging Bacillus cereus Staphylococcus aureus Discriminant Analysis Food Contamination High-Throughput Screening Assays Food Microbiology Foodborne Diseases Principal Component Analysis Neural Networks, Computer Deep Learning Support Vector Machine Escherichia coli
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CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.

Aquatic toxicology (Amsterdam, Netherlands) Jan 7, 2025
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of di...
Water Pollutants, Chemical Neural Networks, Computer Aquatic Organisms Deep Learning Supervised Machine Learning
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Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jan 7, 2025
Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the...
Humans Deep Learning Image Processing, Computer-Assisted Mass Spectrometry Algorithms Animals
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High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry.

Journal of chemical information and modeling Jan 7, 2025
Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining ma...
Quantum Theory Antioxidants Structure-Activity Relationship Peptides Deep Learning
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Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling Jan 7, 2025
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulat...
Peptides Deep Learning Molecular Dynamics Simulation RNA Nucleic Acid Conformation Protein Binding RNA, Viral
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