Yanjun Qi
ABSTRACT
The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of many molecular elements on human genomes. This massive-scale "-omics" data provides researchers with an unprecedented opportunity to understand gene regulation that can enable new insights into principles of life, the study of diseases, and the development of treatments and drugs. Computational challenges are the major bottlenecks for comprehensive genome-wide data analysis of gene regulation. Such data sets are complex, structured and at an unprecedented scale of data growth. Problems of this nature may be particularly well suited to deep learning techniques that recently show impressive results across a variety of domains. This tutorial aims to provide an extensive literature review about the state-of-the-art techniques in deep Learning, to examine how deep learning is enabling changes at analyzing datasets about gene regulations, and to foresee the potential of deep learning to transform several areas of biology and medicine.
Index Terms
- ACM-BCB'18 Tutorial: Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation
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