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TargetAnalytica: A Text Analytics Framework for Ranking Therapeutic Molecules in the Bibliome

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 77))

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Abstract

Biomedical scientists often search databases of therapeutic molecules to answer a set of molecule-related questions. When it comes to drugs, finding the most specific target is a crucial biological criterion. Whether the target is a gene, protein, and cell line, target specificity is what makes a therapeutic molecule significant. In this chapter, we present TargetAnalytica, a novel text analytics framework that is concerned with mining the biomedical literature. Starting with a set of publications of interest, the framework produces a set of biological entities related to gene, protein, RNA, cell type, and cell line. The framework is tested against a depression-related dataset for the purpose of demonstration. The analysis shows an interesting ranking that is significantly different from a counterpart based on drugs.com’s popularity factor (e.g., according to our analysis Cymbalta appears only at position #10 though it is number one in popularity according to the database). The framework is a crucial tool that identifies the targets to investigate, provides relevant specificity insights, and help decision makers and scientists to answer critical questions that are not possible otherwise.

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Acknowledgements

The authors would like thank Dr. Mark Schreiber and Dr. Ramiro Barrantes for their valuable discussions. The authors also greatly appreciate the tremendous feedback on this work giving by Dr. Barabasi and his lab members.

Author information

Authors and Affiliations

  1. Norwich University, Northfield, VT, USA

    Ahmed Abdeen Hamed

  2. Merck and Co., Inc., Boston, MA, USA

    Ahmed Abdeen Hamed & Megean Schoenberg

  3. MSD, Prague, Czech Republic

    Agata Leszczynska & Gergely Temesi

  4. The University of Melbourne, Melbourne, Australia

    Karin Verspoor

Authors
  1. Ahmed Abdeen Hamed
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  2. Agata Leszczynska
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  3. Megean Schoenberg
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  4. Gergely Temesi
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  5. Karin Verspoor
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Correspondence to Ahmed Abdeen Hamed .

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Editors and Affiliations

  1. Faculty of Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Abdeen Hamed, A., Leszczynska, A., Schoenberg, M., Temesi, G., Verspoor, K. (2021). TargetAnalytica: A Text Analytics Framework for Ranking Therapeutic Molecules in the Bibliome. In: Hassanien, A.E., Darwish, A. (eds) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-59338-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-59338-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59337-7

  • Online ISBN: 978-3-030-59338-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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