Genome-wide association study on color-image-based convolutional neural networks
Background Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome-wide association studies. Existing studies convert...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-01-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/18822.pdf |
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| Summary: | Background Convolutional neural networks have excellent modeling abilities to complex large-scale datasets and have been applied to genomics. It requires converting genotype data to image format when employing convolutional neural networks to genome-wide association studies. Existing studies converting the data into grayscale images have shown promising. However, the grayscale image may cause the loss of information of the genotype data. Methods In order to make full use of the information, we proposed a new method, color-image-based convolutional neural networks, by converting the data into color images. Results The experiments on simulation and real data show that our method outperforms the existing methods proposed by Yue and Chen for converting data into grayscale images, in which the model accuracy is improved by an average of 7.61%, and the ratio of disease risk genes is increased by an average of 18.91%. The new method has better robustness and generalized performance. |
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| ISSN: | 2167-8359 |