Invited talk: Developing deep multi-source intelligent learning that facilitates the advancement of single cell genomics research

Thanks to recent advances in the field of genomics, it is now possible to create a comprehensive atlas of the basic units of life-cells. In this paper, we present a frame work for single cell genomics research which employs several new machine learning models such as convolutional neural networks, deep auto-encoder, recurrent neural networks etc. With these effective learning models on multi-source data, such as biomedical literatures and cell images, we can achieve novel cell types and functional gene sets.