UC Irvine

Deep learning has been applied to solve complex problems in a variety of scientific domains including biological and medical sciences. In particular, convolutional neural networks (CNNs) have displayed state-of-the-art results in computer vision tasks, outperforming humans in numerous cases. In this work, we explore several deep learning applications in medicine and bioinformatics. We show that deep learning can be applied to real-world medical procedures such as colon screening, and assist physicians with polyp detection and segmentation, and that deep learning can be applied to optical surgery, and assist physicians with image quality enhancement. In the field of bioinformatics, advances in high-throughput sequencing technologies have greatly lowered the cost of genome-wide sequencing, allowing researchers to include a larger number of experimental replicates than they previous could. This allows an easier application of deep learning as well as other statistical models to solve the problems in bioinformatics, such as detecting circadian rhythm in high-throughput omic data. To access and mine circadian datasets in a comprehensive and integrated way, we curate over 227 high-thoughput circadian datasets across different species and tissues, apply a deep learning model named BIO_CYCLE together with other statistical methods to detect circadian pattern in the omic data, and build a web portal http://circadiomics.ics.uci.edu for search and visualization.