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

Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian and Yi Pan
Volume: 13, No: 2, Page: 204 ~ 214, Year: 2017
10.3745/JIPS.04.0029
Keywords: Bioinformatics, Deep Learning, Deep Neural Networks, DNA Genome Analysis, Image Data Analysis, Machine Learning, lincRNA
Full Text:

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.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.


Cite this article
IEEE Style
Ning Yu, Zeng Yu, Feng Gu, Tianrui Li, Xinmin Tian, and Yi 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, "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.