Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches


Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian, Yi Pan, Journal of Information Processing Systems Vol. 13, No. 2, pp. 204-214, Apr. 2017  

https://doi.org/10.3745/JIPS.04.0029
Keywords: Bioinformatics, Deep Learning, Deep Neural Networks, DNA Genome Analysis, Image Data Analysis, Machine Learning, lincRNA
Fulltext:

Abstract

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.


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Cite this article
[APA Style]
Yu, N., Yu, Z., Gu, F., Li, T., Tian, X., & Pan, Y. (2017). Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches. Journal of Information Processing Systems, 13(2), 204-214. DOI: 10.3745/JIPS.04.0029.

[IEEE Style]
N. Yu, Z. Yu, F. Gu, T. Li, X. Tian, Y. Pan, "Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches," Journal of Information Processing Systems, vol. 13, no. 2, pp. 204-214, 2017. DOI: 10.3745/JIPS.04.0029.

[ACM Style]
Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian, and Yi Pan. 2017. Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches. Journal of Information Processing Systems, 13, 2, (2017), 204-214. DOI: 10.3745/JIPS.04.0029.