AIMC Topic: Deep Learning
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An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).
Scientific reports
Apr 16, 2025
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of el...
Big Data
Diarrhea
ROC Curve
Area Under Curve
Feasibility Studies
Drug-Related Side Effects and Adverse Reactions
Humans
Reproducibility of Results
Deep Learning
Electrodiagnosis
Digestive System Diseases
Predictive Learning Models
Vomiting
Databases, Pharmaceutical
Gastrointestinal Tract
Biological Clocks
Constipation
Nausea
DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model.
Scientific reports
Apr 16, 2025
Violence recognition in surveillance videos is a vital feature of current security systems. It can improve complete security measures, making it essential in generating safe public places and defending against unforeseen security tasks. Real risk and...
TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.
Nature communications
Apr 16, 2025
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...
A deep learning approach for quantifying CT perfusion parameters in stroke.
Biomedical physics & engineering express
Apr 16, 2025
. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residu...
Emerging frontiers in protein structure prediction following the AlphaFold revolution.
Journal of the Royal Society, Interface
Apr 16, 2025
Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated ...
Optimizing lipocalin sequence classification with ensemble deep learning models.
PloS one
Apr 16, 2025
Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To addr...
Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.
PloS one
Apr 16, 2025
Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an ex...
Deep learning-based acceleration of muscle water T2 mapping in patients with neuromuscular diseases by more than 50% - translating quantitative MRI from research to clinical routine.
PloS one
Apr 16, 2025
BACKGROUND: Quantitative muscle water T2 (T2w) mapping is regarded as a biomarker for disease activity and response to treatment in neuromuscular diseases (NMD). However, the implementation in clinical settings is limited due to long scanning times a...
Development and application of deep learning-based diagnostics for pathologic diagnosis of gastric endoscopic submucosal dissection specimens.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Apr 15, 2025
BACKGROUND: Accurate diagnosis of ESD specimens is crucial for managing early gastric cancer. Identifying tumor areas in serially sectioned ESD specimens requires experience and is time-consuming. This study aimed to develop and evaluate a deep learn...
Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection.
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Apr 15, 2025
BACKGROUND: We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict the survival of patients with gastric cancer (GC).
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