Design of a Flexible, User Friendly Feature Matrix Generation System and its Application on Biomedical Datasets

• Published in: Journal of grid computing 2020-09, Vol.18 (3), p.507-527
• Main Authors: Ghorbani, M., Swift, S., Taylor, S. J. E., Payne, A. M.
• Format: Article
• Language: English
• Subjects: Computer Science; Management of Computing and Information Systems; Processor Architectures; User Interfaces and Human Computer Interaction
• ISSN: 1570-7873; 1572-9184
• DOI: 10.1007/s10723-020-09518-y

The generation of a feature matrix is the first step in conducting machine learning analyses on complex data sets such as those containing DNA, RNA or protein sequences. These matrices contain information for each object which have to be identified using complex algorithms to interrogate the data. They are normally generated by combining the results of running such algorithms across various datasets from different and distributed data sources. Thus for non-computing experts the generation of such matrices prove a barrier to employing machine learning techniques. Further since datasets are becoming larger this barrier is augmented by the limitations of the single personal computer most often used by investigators to carry out such analyses. Here we propose a user friendly system to generate feature matrices in a way that is flexible, scalable and extendable. Additionally by making use of The Berkeley Open Infrastructure for Network Computing (BOINC) software, the process can be speeded up using distributed volunteer computing possible in most institutions. The system makes use of a combination of the Grid and Cloud User Support Environment (gUSE), combined with the Web Services Parallel Grid Runtime and Developer Environment Portal (WS-PGRADE) to create workflow-based science gateways that allow users to submit work to the distributed computing. This report demonstrates the use of our proposed WS-PGRADE/gUSE BOINC system to identify features to populate matrices from very large DNA sequence data repositories, however we propose that this system could be used to analyse a wide variety of feature sets including image, numerical and text data.