AIMC Topic: Deep Learning

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Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of chemical information and modeling Apr 1, 2025
Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthost...
Cyclin-Dependent Kinase 2 Allosteric Regulation Molecular Docking Simulation Drug Design Deep Learning Ligands Protein Conformation Protein Kinase Inhibitors
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Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model.

Cell genomics Apr 1, 2025
Signaling pathway components are well studied, but how they mediate cell-type-specific transcription responses is an unresolved problem. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show that the DNA binding of signaling eff...
Animals Hippo Signaling Pathway Deep Learning Protein Binding DNA-Binding Proteins Trophoblasts Transcription Factors YAP-Signaling Proteins Mice Molecular Dynamics Simulation Enhancer Elements, Genetic Protein Serine-Threonine Kinases Signal Transduction TEA Domain Transcription Factors Phosphoproteins Muscle Proteins Adaptor Proteins, Signal Transducing
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Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

Toxins Apr 1, 2025
The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine le...
Inactivation, Metabolic Deep Learning Biodegradation, Environmental Neural Networks, Computer Enzymes Machine Learning
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Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators.

Journal of environmental management Apr 1, 2025
Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of w...
Greenhouse Gases Deep Learning Waste Disposal, Fluid Water Quality Neural Networks, Computer Wastewater
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DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

BMC bioinformatics Apr 1, 2025
BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainst...
Humans Software Deep Learning Computational Biology Chromatin Chromatin Immunoprecipitation Sequencing
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Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs.

BMC ophthalmology Apr 1, 2025
PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ResNet-101, for screening thyroid eye disease (TED) using frontal face photographs, and to test these models under clinical conditions.
Face Photography Adult Graves Ophthalmopathy Middle Aged Humans Sensitivity and Specificity Female Deep Learning Neural Networks, Computer Male Aged ROC Curve
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Automatic detection of developmental stages of molar teeth with deep learning.

BMC oral health Apr 1, 2025
BACKGROUND: The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.
Tooth Germ Child Radiography, Panoramic Molar Young Adult Adult Male Deep Learning Adolescent Child, Preschool Humans Neural Networks, Computer Female
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Detection of kidney bean leaf spot disease based on a hybrid deep learning model.

Scientific reports Apr 1, 2025
Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages,...
Machine Learning Algorithms Phaseolus Deep Learning Plant Leaves Plant Diseases
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Improved gated recurrent unit-based osteosarcoma prediction on histology images: a meta-heuristic-oriented optimization concept.

Scientific reports Apr 1, 2025
The major prevalent primary bone cancer is osteosarcoma. Preoperative chemotherapy is accompanied by resection as part of the normal course of treatment. The diagnosis and treatment of patients are based on the chemotherapy reaction. Contrarily, chem...
Humans Algorithms Osteosarcoma Bone Neoplasms Discriminant Analysis Deep Learning Image Processing, Computer-Assisted
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Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Apr 1, 2025
We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelli...
Artificial Intelligence Female Aged Humans Coronary Artery Disease Tomography, Emission-Computed, Single-Photon Myocardial Perfusion Imaging Male Middle Aged Deep Learning
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