Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method

Since complex diseases such as cancer, diabetes and so on pose a very big threat to human health, they have been extensively studied in the past decades1. However, the underlying pathogenesis of complex diseases is still not clearly known. With the rapid development of genomics technologies, the big data of variations on DNA level such as SNP and CNV (copy number variation) allow comprehensive characterization of complex diseases and provide potential biomarkers to predict the status of complex diseases. Due to the'missing heritability' and lack of reproducibility, the exploration of relationships between SNPs and complex diseases have been transferred from single variation to biomarkers interactions which are defined as epistasis2. First, as the number of variants increases, the combination space expands exponentially, resulting in the'curse of dimensionality' problem.