<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We developed a procedure for estimating competitive fitness by using <italic>Caenorhabditis elegans</italic> as a model organism and a Convolutional Neural Network (CNN) as a tool. Competitive fitness is usually the most informative fitness measure, and competitive fitness assays often rely on green fluorescent protein (GFP) marker strains. CNNs are a class of deep learning neural networks, which are well suited for image analysis and object classification. Our model analyses involved image classification of nematodes as wild-type vs. GFP-expressing, and counted both categories. The performance was analyzed with (i) precision and recall parameters, and (ii) comparison of the wild-type frequency calculated from the model against that obtained by visual scoring of the same images. The average precision and recall varied from 0.79 to 0.87 and from 0.84 to 0.92, respectively, depending on worm density in the images. Compared with manual counting, the model decreased counting time at least 20-fold while preventing human errors. Given the rapid development in the field of CNN, the model, which is fully available on GitHub, can be further optimized and adapted for other image-based uses.</p>
</abstract>

We developed a procedure for estimating competitive fitness by using Caenorhabditis elegans as a model organism and a Convolutional Neural Network (CNN) as a tool. Competitive fitness is usually the most informative fitness measure, and competitive fitness assays often rely on green fluorescent protein (GFP) marker strains. CNNs are a class of deep learning neural networks, which are well suited for image analysis and object classification. Our model analyses involved image classification of nematodes as wild-type vs. GFP-expressing, and counted both categories. The performance was analyzed with (i) precision and recall parameters, and (ii) comparison of the wild-type frequency calculated from the model against that obtained by visual scoring of the same images. The average precision and recall varied from 0.79 to 0.87 and from 0.84 to 0.92, respectively, depending on worm density in the images. Compared with manual counting, the model decreased counting time at least 20-fold while preventing human errors. Given the rapid development in the field of CNN, the model, which is fully available on GitHub, can be further optimized and adapted for other image-based uses.

Competitive fitness analysis using Convolutional Neural Network

Journal of Nematology

This work is licensed under the Creative Commons Attribution 4.0 International License.

running-head

Competitive fitness analysis by using CNN models:

We developed a procedure for estimating competitive fitness by using Caenorhabditis elegans as a model organism and a Convolutional Neural Network (CNN) as a...

Competitive fitness analysis using Convolutional Neural Network

Publié en ligne: 06 nov. 2020

Pages: 1 - 15

DOI: https://doi.org/10.21307/jofnem-2020-108

Mots clés
Competitive fitness, Convolutional neural network, Fitness method

© 2020 Joanna K. Palka et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Competitive fitness analysis using Convolutional Neural Network

Publié en ligne: 06 nov. 2020

Pages: 1 - 15

DOI: https://doi.org/10.21307/jofnem-2020-108

Mots clésCompetitive fitness, Convolutional neural network, Fitness method

© 2020 Joanna K. Palka et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Competitive fitness, Convolutional neural network, Fitness method