AIMC Topic: Deep Learning

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Research and application progress in deep learning in otology.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
With the optimization of deep learning algorithms and the accumulation of medical big data, deep learning technology has been widely applied in research in various fields of otology in recent years. At present, research on deep learning in otology is...

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

Briefings in bioinformatics
The adaptive immune response to foreign antigens is initiated by T-cell receptor (TCR) recognition on the antigens. Recent experimental advances have enabled the generation of a large amount of TCR data and their cognate antigenic targets, allowing m...

miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes.

Briefings in bioinformatics
Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performanc...

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

Briefings in bioinformatics
With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-cons...

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Briefings in bioinformatics
Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep lear...

Identification of metal ion-binding sites in RNA structures using deep learning method.

Briefings in bioinformatics
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA struct...

AD-Syn-Net: systematic identification of Alzheimer's disease-associated mutation and co-mutation vulnerabilities via deep learning.

Briefings in bioinformatics
Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsib...

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Briefings in bioinformatics
Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair res...

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Briefings in bioinformatics
Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understandin...

DIST: spatial transcriptomics enhancement using deep learning.

Briefings in bioinformatics
Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we pres...