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Using the Duplication-Divergence Network Model to Predict Protein-Protein Interactions

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Complex Networks and Their Applications XI (COMPLEX NETWORKS 2016 2022)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1077))

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Abstract

Interactions between proteins are key to most biological processes, but thorough testing can be costly in terms of money and time. Computational approaches for predicting such interactions are an important alternative. This study presents a novel approach to this prediction using calibrated synthetic networks as input for training a decision tree ensemble model with relevant topological information. This trained model is later used for predicting interactions on the human interactome, as a case study. Results show that deterministic metrics perform better than their stochastic counterparts, although a random forest model shows a feature combination case with comparable precision results.

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Acknowledgments

This work was funded by the OMICAS program: Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (Infraestructura y Validación en Arroz y Caña de Azúcar), anchored at the Pontificia Universidad Javeriana in Cali and funded within the Colombian Scientific Ecosystem by The World Bank, the Colombian Ministry of Science, Technology and Innovation, the Colombian Ministry of Education, and the Colombian Ministry of Industry and Tourism, and ICETEX, under GRANT ID: FP44842-217-2018.

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Authors and Affiliations

  1. Department of Electronics and Computer Science, Pontificia Universidad Javeriana, Cali, Colombia

    Nicolás López-Rozo, Jorge Finke & Camilo Rocha

Authors
  1. Nicolás López-Rozo
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  2. Jorge Finke
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  3. Camilo Rocha
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Corresponding author

Correspondence to Nicolás López-Rozo .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon, France

    Hocine Cherifi

  2. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Rosario Nunzio Mantegna

  3. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  4. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  5. Dipartimento di Fisica e Chimica Emilio Segrè, Università degli Studi Palermo, Palermo, Italy

    Salvatore Miccichè

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López-Rozo, N., Finke, J., Rocha, C. (2023). Using the Duplication-Divergence Network Model to Predict Protein-Protein Interactions. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-21127-0_27

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