AIMC Topic: Deep Learning

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HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

Briefings in bioinformatics
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identifica...

Hierarchical multimodal self-attention-based graph neural network for DTI prediction.

Briefings in bioinformatics
Drug-target interactions (DTIs) are a key part of drug development process and their accurate and efficient prediction can significantly boost development efficiency and reduce development time. Recent years have witnessed the rapid advancement of de...

Morphological profiling for drug discovery in the era of deep learning.

Briefings in bioinformatics
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the sin...

Improved prediction of DNA and RNA binding proteins with deep learning models.

Briefings in bioinformatics
Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in essential biological processes. To facilitate functional annotation and accurate prediction of different types of NA...

sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Briefings in bioinformatics
MOTIVATION: Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure f...

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Briefings in bioinformatics
Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies h...

Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.

Cancer research communications
UNLABELLED: Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (M...

Development and external validation of deep learning clinical prediction models using variable-length time series data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected...

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are tran...

An interpretable predictive deep learning platform for pediatric metabolic diseases.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the de...