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Artificial Image Objects For Classification Of Schizophrenia With Gwas-Selected Snvs And Convolutional Neural Network, Xiangning Chen, Daniel G. Chen, Zhongming Zhao, Justin Zhan, Changrong Ji, Jingchun Chen

School of Medicine Faculty Publications

In this article, we propose a new approach to analyze large genomics data. We considered individual genetic variants as pixels in an image and transformed a collection of variants into an artificial image object (AIO), which could be classified as a regular image by CNN algorithms. Using schizophrenia as a case study, we demonstrate the principles and their applications with 3 datasets. With 4,096 SNVs, the CNN models achieved an accuracy of 0.678 ± 0.007 and an AUC of 0.738 ± 0.008 for the diagnosis phenotype. With 44,100 SNVs, the models achieved class-specific accuracies of 0.806 ± 0.032 and 0.820 …