Methods and Trends in Information Retrieval in Big Data Genomic Research
Joseph M. De Guia1, Madhavi Deveraj2
1Joseph M. De Guia, School of Information Technology, Mapua University, Muralla St., Intramuros, Manila, Philippines.
2Madhavi Deveraj, School of Information Technology, Mapua University, Muralla St., Intramuros, Manila, Philippines.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 515-523 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11090789S219/19©BEIESP DOI: 10.35940/ijitee.I1109.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper described information retrieval (IR) and the common methods of finding, extracting, and mining information in genomic research through text mining, and natural language processing (NLP). There was a surge of genomic information from the different literature and the production of genome datasets that catapulted the development of several tools for analyzing and presenting new found knowledge in the biomedical and genome research. This paper presented the recent research trends, survey, reviews, experiments, and concepts in information retrieval applied to text, images and object features in big data genomic research. The method used is exploratory survey research in IR uses in genomic research that presents the concepts, methods, evaluation results and next steps described by the key researchers.
Keywords: Information Retrieval, Text Mining, Natural Language Processing, Big Data, Genome, Genomic Research.
Scope of the Article: Information Ecology and Knowledge Management