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Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy

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Advances in Artificial Intelligence, Computation, and Data Science

Part of the book series: Computational Biology ((COBO,volume 31))

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Abstract

Precision medicine has emerged to tailor clinical decisions based on patient genetic features in a personalized healthcare perspective. The ultimate goal is to drive disease diagnosis and treatment selection based on the patient molecular profiles, usually given by large volumes of data, which is intrinsically high-dimensional, heterogeneous, noisy, and incomplete. Along with the notable improvement of experimental technologies, statistical learning has accompanied the associated challenges by the significant development of novel methods and algorithms. In particular, network-based learning is providing promising results toward more personalized medicine. This short survey will describe three main interconnected trends identified to address these challenges and all with a firm root in network science: differential network analysis, network-based regularization, and causal discovery and inference. An overview of the main applications is provided, along with available software. Biomedical networks support more informed and interpretable statistical learning models from patients’ data, thus improving clinical decisions and supporting therapy optimization.

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Acknowledgements

This work was partially supported by national funds through the Portuguese Foundation for Science & Technology (FCT) with references CEECINST/00102/2018, PTDC/CCI-BIO/4180/2020, DSAIPA/DS/0026/2019, UIDB/00297/2020 (CMA), UIDB/04516/2020 (NOVA LINCS), and UIDB/50021/2020 (INESC-ID), and by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 951970 (OLISSIPO project).

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Authors and Affiliations

  1. NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), Centre for Mathematics and Applications (CMA), Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516, Caparica, Portugal

    Marta B. Lopes

  2. INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol 9, 1000-129, Lisboa, Portugal

    Susana Vinga

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  1. Marta B. Lopes
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  2. Susana Vinga
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Correspondence to Susana Vinga .

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Editors and Affiliations

  1. Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Tuan D. Pham

  2. Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong

    Hong Yan

  3. College of Sciences and Human Studies, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia

    Muhammad W. Ashraf

  4. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

    Folke Sjöberg

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Lopes, M.B., Vinga, S. (2021). Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy. In: Pham, T.D., Yan, H., Ashraf, M.W., Sjöberg, F. (eds) Advances in Artificial Intelligence, Computation, and Data Science. Computational Biology, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-69951-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-69951-2_3

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