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A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining

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Biomedical Text Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2496))

Abstract

The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug–disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist’s perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.

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Author information

Authors and Affiliations

  1. Department of Bioinformatics, Stella Maris College (Autonomous), Chennai, Tamilnadu, India

    Sharanya Manoharan

  2. Department of Sciences, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, Tamilnadu, India

    Oviya Ramalakshmi Iyyappan

Authors
  1. Sharanya Manoharan
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  2. Oviya Ramalakshmi Iyyappan
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Editor information

Editors and Affiliations

  1. Morgridge Institute for Research, University of Wisconsin, Madison, WI, USA

    Kalpana Raja

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Manoharan, S., Iyyappan, O.R. (2022). A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining. In: Raja, K. (eds) Biomedical Text Mining. Methods in Molecular Biology, vol 2496. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2305-3_3

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  • DOI: https://doi.org/10.1007/978-1-0716-2305-3_3

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2304-6

  • Online ISBN: 978-1-0716-2305-3

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