AIMC Topic: Deep Learning

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Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising.

Optics express
We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broad...

DeePVP: Identification and classification of phage virion proteins using deep learning.

GigaScience
BACKGROUND: Many biological properties of phages are determined by phage virion proteins (PVPs), and the poor annotation of PVPs is a bottleneck for many areas of viral research, such as viral phylogenetic analysis, viral host identification, and ant...

Deep learning phase-unwrapping method based on adaptive noise evaluation.

Applied optics
To address the problem of phase unwrapping for interferograms, a deep learning (DL) phase-unwrapping method based on adaptive noise evaluation is proposed to retrieve the unwrapped phase from the wrapped phase. First, this method uses a UNet3+ as the...

Relationship between the kernel size of a convolutional layer and the optical point spread function in ghost imaging using deep learning for identifying defect locations.

Applied optics
We explore the contribution of convolutional neural networks to correcting for the effect of the point spread function (PSF) of the optics when applying ghost imaging (GI) combined with deep learning to identify defect positions in materials. GI can ...

Hierarchical reinforcement learning for automatic disease diagnosis.

Bioinformatics (Oxford, England)
MOTIVATION: Disease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy s...

PScL-DDCFPred: an ensemble deep learning-based approach for characterizing multiclass subcellular localization of human proteins from bioimage data.

Bioinformatics (Oxford, England)
MOTIVATION: Characterization of protein subcellular localization has become an important and long-standing task in bioinformatics and computational biology, which provides valuable information for elucidating various cellular functions of proteins an...

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction f...

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.

Bioinformatics (Oxford, England)
MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive resul...

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.

Journal of the American College of Cardiology
BACKGROUND: Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgita...

Pan-cancer integrative histology-genomic analysis via multimodal deep learning.

Cancer cell
The rapidly emerging field of computational pathology has demonstrated promise in developing objective prognostic models from histology images. However, most prognostic models are either based on histology or genomics alone and do not address how the...