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Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods

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Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, 33720 Tampere, Finland
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Institute of Biosciences and Medical Technology, 33520 Tampere, Finland
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Department of Mechatronics and Biomedical Computer Science, UMIT, 6060 Hall in Tyrol, Austria
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College of Computer and Control Engineering, Nankai University, Tianjin 300071, China
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Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, 4400 Steyr Campus, Austria
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Computational Systems Biology Lab, Tampere University of Technology, 33720 Tampere, Finland
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Institute for Systems Biology, Seattle, WA 98109, USA
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 205-210; https://doi.org/10.3390/make1010012
Received: 26 September 2018 / Revised: 18 November 2018 / Accepted: 21 November 2018 / Published: 22 November 2018
(This article belongs to the Section Network)
We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches. View Full-Text
Keywords: knowledge extraction; computational pharmacogenomics; systems pharmacogenomics; network science; computational biology; genomics; big data; databases knowledge extraction; computational pharmacogenomics; systems pharmacogenomics; network science; computational biology; genomics; big data; databases
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Musa, A.; Dehmer, M.; Yli-Harja, O.; Emmert-Streib, F. Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods. Mach. Learn. Knowl. Extr. 2019, 1, 205-210.

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