AIMC Topic: Deep Learning

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Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.

European heart journal
BACKGROUND AND AIMS: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET f...

YOLOv11-Based quantification and temporal analysis of repetitive behaviors in deer mice.

Neuroscience
Detailed temporal dynamics of deer mouse (Peromyscus maniculatus bairdii) behavior remain poorly characterized. This study presents an integrated automated system combining YOLOv11 deep learning for direct behavior classification, post-processing for...

DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.

Journal of computer-aided molecular design
In drug discovery, virtual screening and repositioning rely on accurate Drug-Target Binding Affinity (DTBA) prediction to develop effective therapies. However, DTBA prediction remains challenging due to limited annotated datasets, high-dimensional bi...

Think deep in the tractography game: deep learning for tractography computing and analysis.

Brain structure & function
Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophistica...

Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.

Aesthetic surgery journal
BACKGROUND: Breast implant illness (BII) is a spectrum of symptoms some people attribute to breast implants. Although causality remains unproven, patient interest has grown significantly. Understanding patient perceptions of BII on social media is cr...

Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.

Neurobiology of disease
BACKGROUND: Despite prior studies on early-stage Parkinson's disease (PD) brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes remain poorly understood. Our ...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...

PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...

A multimodal deep learning model for detecting endoscopic images of near-infrared fluorescence capsules.

Biosensors & bioelectronics
Early screening for gastrointestinal (GI) diseases is critical for preventing cancer development. With the rapid advancement of deep learning technology, artificial intelligence (AI) has become increasingly prominent in the early detection of GI dise...