KAUST DepartmentComputer Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Embargo End Date2021-06-24
Permanent link to this recordhttp://hdl.handle.net/10754/663856
MetadataShow full item record
AbstractDeep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.
CitationLi, H., Tian, S., Li, Y., Fang, Q., Tan, R., Pan, Y., … Gao, X. (2020). Modern Deep Learning in Bioinformatics. Journal of Molecular Cell Biology. doi:10.1093/jmcb/mjaa030
PublisherOxford University Press (OUP)
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc/4.0/
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