Network Module Detection from Multi-Modal Node Features with a Greedy Decision Forest for Actionable Explainable AI

• URL: http://arxiv.org/abs/2108.11674v1
• Date: Thu, 26 Aug 2021 09:42:44 GMT
• Title: Network Module Detection from Multi-Modal Node Features with a Greedy Decision Forest for Actionable Explainable AI
• Authors: Bastian Pfeifer, Anna Saranti and Andreas Holzinger
• Abstract summary: In this work, we demonstrate subnetwork detection based on multi-modal node features using a new Greedy Decision Forest. Our glass-box approach could help to uncover disease-causing network modules from multi-omics data to better understand diseases such as cancer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Network-based algorithms are used in most domains of research and industry in a wide variety of applications and are of great practical use. In this work, we demonstrate subnetwork detection based on multi-modal node features using a new Greedy Decision Forest for better interpretability. The latter will be a crucial factor in retaining experts and gaining their trust in such algorithms in the future. To demonstrate a concrete application example, we focus in this paper on bioinformatics and systems biology with a special focus on biomedicine. However, our methodological approach is applicable in many other domains as well. Systems biology serves as a very good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our glass-box approach could help to uncover disease-causing network modules from multi-omics data to better understand diseases such as cancer.

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