AIMC Topic: Deep Learning

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Leveraging Transformer Models to Capture Multi-Scale Dynamics in Biomolecules by Nano-GPT.

Journal of chemical theory and computation
Long-term biomolecular dynamics is critical for understanding key evolutionary transformations in molecular systems. However, capturing these processes requires extended simulation timescales that often exceed the practical limits of conventional mod...

Deep Learning-Driven Discovery of Novel Antimicrobial Peptides from Large-Scale Protist Genomes and Experimental Characterization.

Journal of chemical information and modeling
The escalating issue of antibiotic resistance has created an urgent global demand within the biomedical field for the discovery of novel antimicrobial molecules as alternatives to traditional antibiotics. Previous studies have reported the identifica...

A comprehensive benchmarking of adaptive sampling tools for nanopore sequencing.

Genome biology
BACKGROUND: Adaptive sampling is an emerging technology to enrich target reads while depleting unwanted reads during real-time nanopore sequencing. The application of different algorithms has spawned various tools for the determination of read reject...

Comparative evaluation of deep learning and traditional models for predicting traffic accident severity in Saudi Arabia.

Scientific reports
Road traffic accidents are one of the leading death causes around the globe, claiming millions of lives every year. Predicting traffic accident severity is essential for road users' safety and accident prevention. Artificial neural network (ANN), Boo...

Explaining solar forecasts with generative AI: A two-stage framework combining transformers and LLMs.

PloS one
Accurate and interpretable solar power forecasting is critical for effectively integrating Photo-Voltaic (PV) systems into modern energy infrastructure. This paper introduces a novel two-stage hybrid framework that couples deep learning-based time se...

Auto-Masked Audio Spectrogram Transformer for depression detection from speech.

Journal of affective disorders
BACKGROUND: Depression is a psychological disorder characterized by altered self-referential cognition and impaired emotional expression. Traditional diagnostic methods can be costly or intrusive, while Speech-based analysis offers an accessible alte...

A deep learning approach based on molecular graph features and residual blocks to predict interaction sites between CircRNA and RBP.

Biochemical and biophysical research communications
CircRNAs are ubiquitously expressed across diverse tissues and cells, playing a pivotal role not only in protein-mediated biological processes but also in disease prevention and therapeutics. RNA-RBP interactions are critical for deciphering gene reg...

Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Protein-ligand binding affinity assessment plays a pivotal role in virtual drug screening, yet conventional data-driven approaches rely heavily on limited protein-ligand crystal structures. Structure-free compound-protein interaction (CPI) methods ha...

HitScreen: A Sequence-Based Drug Virtual Screening Approach Using Data Augmentation and Protein Language Models.

Journal of chemical information and modeling
Sequence-based drug-target interaction (DTI) prediction is an effective approach for identifying potential drug candidates without relying on three-dimensional protein structures. However, current sequence-based methods often suffer from limited gene...

Clinical validation of a deep learning based application for quantitative assessment of dental plaque in fluorescence imaging.

Clinical oral investigations
AIM: Evaluating dental plaque is a fundamental task for periodontal health care, but it is subjective, time-consuming, and cumbersome. Therefore, this study aimed to develop and validate a web-based deep learning application capable of objectively qu...