AIMC Topic: Deep Learning

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Deep learning-assisted 10-μL single droplet-based viscometry for human aqueous humor.

Biosensors & bioelectronics
Probing the viscosity of human aqueous humor is crucial for optimizing micro-tube shunts in glaucoma treatment. However, conventional viscometers are not suitable for aqueous humor due to the limited sample volume-only tens of microliters-that can be...

Intelligent transformation of ultrasound-assisted novel solvent extraction plant active ingredients: Tools for machine learning and deep learning.

Food chemistry
Ultrasound-assisted novel solvent extraction enhances plant bioactive compound yield via cavitation, mechanical, and thermal mechanisms. However, the high designability of novel solvents, the multiple influence factors for extracting results, the com...

DeepPhosPPI: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein-protein interactions.

Briefings in bioinformatics
Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and c...

PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets.

Journal of evolutionary biology
The digitization of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However,...

Federated Deep Learning Enables Cancer Subtyping by Proteomics.

Cancer discovery
UNLABELLED: Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a federated deep learning approach (ProCanFDL), t...

Classifying the AMi-Br Mitotic Figure Dataset with AUCMEDI.

Studies in health technology and informatics
INTRODUCTION: Mitotic figure (MF) density has been established as a key biomarker for certain tumors. Recently, the differentiation between atypical MFs (AMF) and normal MFs (NMFs) has gained increased interest in research, as AMFs density could be a...

Use of Client-Side Machine Learning Models for Privacy-Preserving Healthcare Predictions - A Deployment Case Study.

Studies in health technology and informatics
INTRODUCTION: Machine learning (ML) and deep learning (DL) models in healthcare traditionally rely on server-centric architectures, where sensitive patient data is transmitted to external servers for processing via frameworks like Flask, raising sign...

Implementation of convolutional neural networks for microbial colony recognition.

Microbiology spectrum
Initial classification of microorganisms based on visual identification of colonies remains challenging for skilled microbiologists and is influenced by the proficiency and subjective interpretation of professionals. To overcome these challenges, we ...

DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy.

Biology of the cell
Deep learning methods using convolutional neural networks are very effective for automatic image segmentation tasks with no exception for cellular electron micrographs. However, the lack of dedicated easy-to-use tools largely reduces the widespread u...

Mapping QTLs for PHS resistance and development of a deep learning model to measure PHS rate in japonica rice.

The plant genome
Rice (Oryza sativa L.) is a staple food for more than half of the global population. Preharvest sprouting (PHS), which reduces yield and grain quality, presents a major challenge for rice production. The development of PHS-resistant varieties is a ma...