Advances in big data bio analytics

Angelopoulos, Nicos ORCID: https://orcid.org/0000-0002-7507-9177 and Wielemaker, Jan 2019. Advances in big data bio analytics. Presented at: 35th International Conference on Logic Programming (CLP 2019), Las Cruces, NM, USA, 20-25 September 2019. Proceedings 35th International Conference on Logic Programming (Technical Communications). Electronic Proceedings in Theoretical Computer Science. , vol.306 ETCS, 309–322.

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Abstract

Delivering effective data analytics is of crucial importance to the interpretation of the multitude of biological datasets currently generated by an ever increasing number of high throughput techniques. Logic programming has much to offer in this area. Here, we detail advances that highlight two of the strengths of logical formalisms in developing data analytic solutions in biological settings: access to large relational databases and building analytical pipelines collecting graph information from multiple sources. We present significant advances on the bio_db package which serves biological databases as Prolog facts that can be served either by in-memory loading or via database backends. These advances include modularising the underlying architecture and the incorporation of datasets from a second organism (mouse). In addition, we introduce a number of data analytics tools that operate on these datasets and are bundled in the analysis package: bio_analytics. Emphasis in both packages is on ease of installation and use. We highlight the general architecture of our components based approach. An experimental graphical user interface via SWISH for local installation is also available. Finally, we advocate that biological data analytics is a fertile area which can drive further innovation in applied logic programming.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	ETCS
Date of First Compliant Deposit:	5 August 2020
Date of Acceptance:	20 June 2019
Last Modified:	04 Jan 2023 02:20
URI:	https://orca.cardiff.ac.uk/id/eprint/133972

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