Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation
In recent decades, assays with the nematode Caenorhabditis elegans (C. elegans) have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous C. elegans assay automation techniques are being developed to in...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022005906 |
| _version_ | 1827577671620493312 |
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| author | Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón |
| author_facet | Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón |
| author_sort | Antonio García-Garví |
| collection | DOAJ |
| description | In recent decades, assays with the nematode Caenorhabditis elegans (C. elegans) have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous C. elegans assay automation techniques are being developed to increase throughput and accuracy. In this paper, a method for predicting the lifespan of C. elegans nematodes using a bimodal neural network is proposed and analyzed. Specifically, the model uses the sequence of images and the count of live C. elegans up to the current day to predict the lifespan curve termination. This network has been trained using a simulator to avoid the labeling costs of training such a model. In addition, a method for estimating the uncertainty of the model predictions has been proposed. Using this uncertainty, a criterion has been analyzed to decide at what point the assay could be halted and the user could rely on the model’s predictions. The method has been analyzed and validated using real experiments. The results show that uncertainty is reduced from the mean lifespan and that most of the predictions obtained do not present statistically significant differences with respect to the curves obtained manually. |
| first_indexed | 2024-03-08T21:31:13Z |
| format | Article |
| id | doaj.art-67130feda2554b93839ff438bde615c2 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:13Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-67130feda2554b93839ff438bde615c22023-12-21T07:30:35ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-0121655664Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimationAntonio García-Garví0Pablo E. Layana-Castro1Antonio-José Sánchez-Salmerón2Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, SpainInstituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, SpainCorresponding author.; Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, SpainIn recent decades, assays with the nematode Caenorhabditis elegans (C. elegans) have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous C. elegans assay automation techniques are being developed to increase throughput and accuracy. In this paper, a method for predicting the lifespan of C. elegans nematodes using a bimodal neural network is proposed and analyzed. Specifically, the model uses the sequence of images and the count of live C. elegans up to the current day to predict the lifespan curve termination. This network has been trained using a simulator to avoid the labeling costs of training such a model. In addition, a method for estimating the uncertainty of the model predictions has been proposed. Using this uncertainty, a criterion has been analyzed to decide at what point the assay could be halted and the user could rely on the model’s predictions. The method has been analyzed and validated using real experiments. The results show that uncertainty is reduced from the mean lifespan and that most of the predictions obtained do not present statistically significant differences with respect to the curves obtained manually.http://www.sciencedirect.com/science/article/pii/S2001037022005906C. elegansLifespan predictionDeep learningSynthetic dataMultimodal neural network |
| spellingShingle | Antonio García-Garví Pablo E. Layana-Castro Antonio-José Sánchez-Salmerón Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation Computational and Structural Biotechnology Journal C. elegans Lifespan prediction Deep learning Synthetic data Multimodal neural network |
| title | Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| title_full | Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| title_fullStr | Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| title_full_unstemmed | Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| title_short | Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| title_sort | analysis of a c elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation |
| topic | C. elegans Lifespan prediction Deep learning Synthetic data Multimodal neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022005906 |
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