Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.
Genet Epidemiol
; 43(3): 330-341, 2019 04.
Article
em En
| MEDLINE
| ID: mdl-30614068
ABSTRACT
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environmental perturbations. With a large number of images generated by high-content microscopy while manual labeling is both labor-intensive and error-prone, machine learning offers a viable alternative for automatic labeling of subcellular localizations. Contrarily, in recent years applications of deep learning methods to large datasets in natural images and other domains have become quite successful. An appeal of deep learning methods is that they can learn salient features from complicated data with little data preprocessing. For such purposes, we applied several representative types of deep convolutional neural networks (CNNs) and two popular ensemble methods, random forests and gradient boosting, to predict protein subcellular localization with a moderately large cell image data set. We show a consistently better predictive performance of CNNs over the two ensemble methods. We also demonstrate the use of CNNs for feature extraction. In the end, we share our computer code and pretrained models to facilitate CNN's applications in genetics and computational biology.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Redes Neurais de Computação
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Proteínas de Saccharomyces cerevisiae
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Aprendizado Profundo
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Microscopia
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Genet Epidemiol
Assunto da revista:
EPIDEMIOLOGIA
/
GENETICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article