AIMC Topic: Deep Learning

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Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.

GigaScience
BACKGROUND: Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations prese...

IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning.

GigaScience
BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and...

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Translational vision science & technology
PURPOSE: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

Translational vision science & technology
PURPOSE: To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Bioinformatics (Oxford, England)
MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the developmen...

Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in a retrospective cohort study with a large sample size. We analyzed the association between depr...

Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodolo...

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances...

Enhancing Wearable Sensor Data Classification Through Novel Modified- Recurrent Plot-Based Image Representation and Mixup Augmentation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it ...

PathSAM: Enhancing Oral Cancer Detection with Advanced Segmentation and Explainability.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Building on the success of the Segment Anything Model (SAM) in image segmentation, "PathSAM: SAM for Pathological Images in Oral Cancer Detection" addresses the unique challenges associated with diagnosing oral cancer. Although SAM is versatile, its ...