Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Evaluating convolutional neural networks and transformer architectures for image-based prediction of protein localization in eukaryotic cells
Background: Accurate prediction of protein subcellular localization is critical for understanding protein function and guiding experimental research. Recent advances in deep learning have enabled high-throughput image-based methods to tackle this problem by leveraging large-scale immunofluorescence...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English English |
| Published: |
Department of Integrative Biomedical Sciences (IBMS)
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Evaluating convolutional neural networks and transformer architectures for image-based prediction of protein localization in eukaryotic cells
- Convolutional and fully convolutional neural networks for the detection of landmarks in tsetse fly wing images
- Text detection in natural images using convolutional neural networks
- A deep convolutional neural network architecture for image classification
- A model for conflicts’ prediction using deep neural network
- Analysing retinal fundus images with deep learning models
- Predicting customer churn in telecommunication industry using convolutional neural network model