Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

Pfeifer, Bastian; Chereda, Hryhorii; Martin, Roman; Saranti, Anna; Angerschmid, Alessa; Clemens, Sandra; Hauschild, Anne-Christin; Beißbarth, Tim; Holzinger, Andreas; Heider, Dominik

doi:10.1101/2023.03.22.533772

Cited by 3 publications

References 15 publications

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Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Holzinger,

Saranti,

Hauschild

et al. 2023

Lecture Notes in Computer Science

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We explore the integration of domain knowledge graphs into Deep Learning for improved interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph’s nodes. Subnetworks that are relevant to the classification (referred to as “disease subnetworks”) are detected using explainable AI. Federated learning is enabled by dividing the knowledge graph into relevant subnetworks, constructing an ensemble classifier, and allowing domain experts to analyze and manipulate detected subnetworks using a developed user interface. Furthermore, the human-in-the-loop principle can be applied with the incorporation of experts, interacting through a sophisticated User Interface (UI) driven by Explainable Artificial Intelligence (xAI) methods, changing the datasets to create counterfactual explanations. The adapted datasets could influence the local model’s characteristics and thereby create a federated version that distils their diverse knowledge in a centralized scenario. This work demonstrates the feasibility of the presented strategies, which were originally envisaged in 2021 and most of it has now been materialized into actionable items. In this paper, we report on some lessons learned during this project.

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Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Holzinger,

Saranti,

Hauschild

et al. 2023

Lecture Notes in Computer Science

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Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

Chereda

Leha

Beißbarth

2021

Preprint

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Motivation: High-throughput technologies play a more and more significant role in discovering prognostic molecular signatures and identifying novel drug targets. It is common to apply Machine Learning (ML) methods to classify high-dimensional gene expression data and to determine a subset of features (genes) that is important for decisions of a ML model. One feature subset of important genes corresponds to one dataset and it is essential to sustain the stability of feature sets across different datasets with the same clinical endpoint since the selected genes are candidates for prognostic biomarkers. The stability of feature selection can be improved by including information of molecular networks into ML methods. Gene expression data can be assigned to the vertices of a molecular network's graph and then classified by a Graph Convolutional Neural Network (GCNN). GCNN is a contemporary deep learning approach that can be applied to graph-structured data. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. In our recent work we developed Graph Layer-wise Relevance Propagation (GLRP) --- a method that adapts LRP to a graph convolution and explains patient-specific decisions of GCNN. GLRP delivers individual molecular signatures as patient-specific subnetworks that are parts of a molecular network representing background knowledge about biological mechanisms. GLRP gives a possibility to deliver the subset of features corresponding to a dataset as well, so that the stability of feature selection performed by GLRP can be measured and compared to that of other methods. Results: Utilizing two large breast cancer datasets, we analysed properties of feature sets selected by GLRP (GCNN+LRP) such as stability and permutation importance. We have implemented a graph convolutional layer of GCNN as a Keras layer so that the SHAP (SHapley Additive exPlanation) explanation method could be also applied to a Keras version of a GCNN model. We compare the stability of feature selection performed by GCNN+LRP to the stability of GCNN+SHAP and to other ML based feature selection methods. We conclude, that GCNN+LRP shows the highest stability among other feature selection methods including GCNN+SHAP. It was established that the permutation importance of features among GLRP subnetworks is lower than among GCNN+SHAP subnetworks, but in the context of the utilized molecular network, a GLRP subnetwork of an individual patient is on average substantially more connected (and interpretable) than a GCNN+SHAP subnetwork, which consists mainly of single vertices.

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A Distinctive Ensemble Approach for Unbiased Movie Recommendations using Variants of Graph Convolution Networks

Mathur,

Arya

2024

Proceedings of the 2024 10th International Conference on Computer Technology Applications

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Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

Cited by 3 publications

References 15 publications

Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

A Distinctive Ensemble Approach for Unbiased Movie Recommendations using Variants of Graph Convolution Networks

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