Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.14.251306v1?rss=1 Authors: Levy, J., Chen, Y., Azizgolshani, N., Petersen, C. L., Titus, A. J., Moen, E. L., Vaickus, L. J., Salas, L. A., Christensen, B. Abstract: DNA methylation (DNAm) alterations are implicated with aging and diseases by regulating gene expression. DNAm deep-learning approaches can capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. We present deep-learning software, MethylCapsNet and MethylSPWNet, that group CpGs into user-specified or predefined biologically relevant groupings (eg. gene promoter or CpG island context) related to diagnostic and prognostic outcomes. We train our models on a cohort (n=3,897) to classify central nervous system tumors and compare to existing approaches. Our methodology presents opportunities to increase interpretability of disease mechanisms through utilization of biologically relevant annotations. Copy rights belong to original authors. Visit the link for more info