Artificial Image Objects for Classification of Schizophrenia with GWAS-Selected SNVs and Convolutional Neural Network
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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 ± 0.049, and AUCs of 0.930 ± 0.017 and 0.867 ± 0.040 for the bottom and top classes stratified by the patient's polygenic risk scores. These results suggest that, once transformed to images, large genomics data can be analyzed effectively with image classification algorithms.
Artificial image objects; Artificial intelligence; Machine learning; Convolutional neural network; Disease risk modeling; GWAS-selected genetic variants; Image classification; Polygenic risk score; Random forest; Schizophrenia classification; Support vector machine
Genetics and Genomics | Genomics | Life Sciences
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Chen, D. G.,
Artificial Image Objects for Classification of Schizophrenia with GWAS-Selected SNVs and Convolutional Neural Network.