AIMC Topic: Deep Learning

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Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Briefings in bioinformatics
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...

PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

Briefings in bioinformatics
The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide ...

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Briefings in bioinformatics
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined eff...

WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Briefings in bioinformatics
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Briefings in bioinformatics
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identificatio...

DeepSinse: deep learning-based detection of single molecules.

Bioinformatics (Oxford, England)
MOTIVATION: Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several...

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Briefings in bioinformatics
Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image proces...

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning.

Stapp car crash journal
Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contac...