We know in recent several years very quickly deep learning (We could see as advanced multi-layer neural network) has achieved success in picture recognition, which could revolutionize medical diagnosis for example neurological disease diagnosis based on fMRI data.
I'm genetics person for complex disease, so I'm wondering if we could ever use deep learning to generate extremely complicated model for those polygenic complex disease? (Or you can say this is genetic biomarker)
For example, given hundreds of thousands of GWAS genotypes, could we generate complicated deep learning model to accurately predict disease? I know many biostatisticians have tried tons using different machine learning technique like: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003264
But deep learning enjoys higher degree of complexity in modeling, so given their success in fMRI, maybe we could try in complex genetics?