Liu Han-Ming, Liu Zhao-Fa, Li Zi, Yu Cong, Hu Peng-Cheng, Liu Qi-Feng, Shi Tai-Gui
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.
Ganzhou Teachers College, Ganzhou, China.
PeerJ. 2025 Jan 13;13:e18822. doi: 10.7717/peerj.18822. eCollection 2025.
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.
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.
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.
卷积神经网络对复杂大规模数据集具有出色的建模能力，并已应用于基因组学。在将卷积神经网络应用于全基因组关联研究时，需要将基因型数据转换为图像格式。现有的将数据转换为灰度图像的研究已显示出良好前景。然而，灰度图像可能会导致基因型数据的信息丢失。
为了充分利用信息，我们提出了一种新方法，即基于彩色图像的卷积神经网络，通过将数据转换为彩色图像来实现。
在模拟数据和真实数据上的实验表明，我们的方法优于Yue和Chen提出的将数据转换为灰度图像的现有方法，其中模型准确率平均提高了7.61%，疾病风险基因的比例平均提高了18.91%。新方法具有更好的稳健性和泛化性能。
