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MolecRank: A Specificity-Based Network Analysis Algorithm

Ranking Therapeutic Molecules in the Bibliome

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Book cover The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) (AMLTA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 921))

Abstract

Biomedical scientists often search databases of therapeutic molecules to answer a set of drug-related queries. In this paper, we present a novel network algorithm called MolecRank that is specialized in searching and ranking molecules using a biomedical literature. Starting with a disease-related set of publications (e.g., depression), a feature extraction step is performed to identify the biological features associated with the drugs of study. The MolecRank is a network centrality algorithm that is specialized in deriving a rank when specificity is in question. The algorithm’s promise is two folds (a) an interesting search-and-rank tool that demonstrated its importance in the drug discovery research, (b) a theoretical network centrality measure that is based on the notion of specificity. We performed our experiments against a depression-related literature dataset. The results shows an interesting order that is significantly different from well-advertised drugs (e.g., Cymbalta#10 though well-advertised). We conclude that not all well-advertised drugs are most specific. This striking evidence highlights the significance of specificity as an important measure in discovering new drugs.

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Acknowledgement

The authors would like thank Greg Temsi, 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. We also thank Dr. Karin Verspoor of University of Melbourne for the valuable discussions.

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Authors and Affiliations

  1. Merck & Co., Inc., Boston, MA, USA

    Ahmed Abdeen Hamed & Mark Schreiber

  2. MSD, Prague, Czech Republic

    Agata Leszczynska

  3. Kaleido Biosciences, Bedford, MA, USA

    Mark Schreiber

Authors
  1. Ahmed Abdeen Hamed
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  2. Agata Leszczynska
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  3. Mark Schreiber
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Corresponding author

Correspondence to Ahmed Abdeen Hamed .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. School of Computing, Science and Engineering, University of Salford, Salford, Greater Manchester, UK

    Tarek Gaber

  4. Department of Computer Science and Engineering, School of Computing and IT, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  5. Faculty of Computer and Information Science, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

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Hamed, A.A., Leszczynska, A., Schreiber, M. (2020). MolecRank: A Specificity-Based Network Analysis Algorithm. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_16

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