AIMC Topic: Deep Learning

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A split-and-merge deep learning approach for phenotype prediction.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Phenotype prediction with genome-wide markers is a critical but difficult problem in biomedical research due to many issues such as nonlinearity of the underlying genetic mapping and high-dimensionality of marker data. When using the deep...

A deep learning framework for characterization of genotype data.

G3 (Bethesda, Md.)
Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for v...

SPEQ: quality assessment of peptide tandem mass spectra with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may...

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in differe...

Impact of Incomplete Blinking Analyzed Using a Deep Learning Model With the Keratograph 5M in Dry Eye Disease.

Translational vision science & technology
PURPOSE: To establish a deep learning model (DLM) for blink analysis, and investigate whether blink video frame sampling rate influences the accuracy of analysis.

Predicting Glaucoma Progression Requiring Surgery Using Clinical Free-Text Notes and Transfer Learning With Transformers.

Translational vision science & technology
PURPOSE: We evaluated the use of massive transformer-based language models to predict glaucoma progression requiring surgery using ophthalmology clinical notes from electronic health records (EHRs).

A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Translational vision science & technology
PURPOSE: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.

A model of speech recognition for hearing-impaired listeners based on deep learning.

The Journal of the Acoustical Society of America
Automatic speech recognition (ASR) has made major progress based on deep machine learning, which motivated the use of deep neural networks (DNNs) as perception models and specifically to predict human speech recognition (HSR). This study investigates...

Hologram classification of occluded and deformable objects with speckle noise contamination by deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
Advancements in optical, computing, and electronic technologies have enabled holograms of physical three-dimensional (3D) objects to be captured. The hologram can be displayed with a spatial light modulator to reconstruct a visible image. Although ho...