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2021 | OriginalPaper | Chapter

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

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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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Metadata
Title
Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy
Authors
Marta B. Lopes
Susana Vinga
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-69951-2_3

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