AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU

Abstract Background The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the...

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Bibliographic Details
Main Authors: Chih-Hao Fang, Nawanol Theera-Ampornpunt, Michael A. Roth, Ananth Grama, Somali Chaterji
Format: Article
Language:English
Published: BMC 2019-10-01
Series:BMC Bioinformatics
Subjects:
Support vector machines (SVM)
Random forest (RF)
Deep neural networks (DNN)
ConvNets (CNN)
Image processing algorithms
Distal regulatory elements
Online Access:http://link.springer.com/article/10.1186/s12859-019-3049-1

Internet

http://link.springer.com/article/10.1186/s12859-019-3049-1

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