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Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis

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Data Science in Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1084))

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Abstract

Essential genes (EGs) are fundamental for the growth and survival of a cell or an organism. Identifying EGs is an important issue in many areas of biomedical research, such as synthetic and system biology, drug development, mechanistic and therapeutic investigations. The essentiality is a context-dependent dynamic attribute of a gene that can vary in different cells, tissues, or pathological conditions, and wet-lab experimental procedures to identify EGs are costly and time-consuming. Commonly explored computational approaches are based on machine learning techniques applied to protein-protein interaction networks, but they are often unsuccessful, especially in the case of human genes. From a biological point of view, the identification of the node essentiality attributes is a challenging task. Nevertheless, from a data science perspective, suitable graph learning approaches still represent an open problem. Node classification in graph modeling/analysis is a machine learning task to predict an unknown node property based on defined node attributes. The model is trained based on both the relationship information and the node attributes. Here, we propose the use of a context-specific integrated network enriched with biological and topological attributes. To tackle the node classification task we exploit different machine and deep learning models. An extensive experimental phase demonstrates the effectiveness of both network structure and attributes associated with the nodes for EGs identification.

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Notes

  1. 1.

    https://bitbucket.org/tuliocampos/essential.

  2. 2.

    https://sites.google.com/site/bctnet/.

  3. 3.

    https://depmap.org/portal/.

  4. 4.

    Scikit-Learn: https://scikit-learn.org/stable/, Pytorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/, Imbalanced-learn: https://imbalanced-learn.org/stable/.

  5. 5.

    Google Colab notebook for result reproducibility are available at: https://github.com/giordamaug/EG-identification---Data-Science-in-App-Springer/tree/main/notebook.

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Acknowledgements

This work has been partially funded by the BiBiNet project (H35F21000430002) within POR-Lazio FESR 2014-2020. It was carried out also within the activities of the authors as members of the ICAR-CNR INdAM Research Unit and partially supported by the INdAM research project “Computational Intelligence methods for Digital Health”. The work of Mario R. Guarracino was conducted within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE). Mario Manzo thanks Prof. Alfredo Petrosino for the guidance and supervision during the years of working together.

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Authors and Affiliations

  1. University of Naples L’Orientale, Via Marina 59, 80131, Naples, Italy

    Mario Manzo

  2. National Research Council (CNR), Via Pietro Castellino 111, 80131, Naples, Italy

    Maurizio Giordano, Lucia Maddalena & Ilaria Granata

  3. National Research University Higher School of Economics, 136 Radionova Ulitsa, Nizhny Novgorod, Russia

    Mario Rosario Guarracino

  4. University of Cassino and Southern Lazio, Campus Folcara, 03043, Cassino, Italy

    Mario Rosario Guarracino

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  1. Mario Manzo
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  2. Maurizio Giordano
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  4. Mario Rosario Guarracino
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Correspondence to Mario Rosario Guarracino .

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  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  2. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Jolita Bernatavičienė

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Manzo, M., Giordano, M., Maddalena, L., Guarracino, M.R., Granata, I. (2023). Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis. In: Dzemyda, G., Bernatavičienė, J., Kacprzyk, J. (eds) Data Science in Applications. Studies in Computational Intelligence, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-031-24453-7_7

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