GNN-SubNet: disease subnetwork detection with explainable graph neural networks

Pfeifer, Bastian; Saranti, Anna; Holzinger, Andreas

doi:10.1093/bioinformatics/btac478

Cited by 34 publications

(22 citation statements)

References 24 publications

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“…For these classifier we do not incorporate any domain-knowledge about the interaction or relatedness of genes. Moreover, we have compared our GDF classifier with a graph neural network approach for disease module detection, which is implemented within the Python package GNN-SubNet 49 ( https://github.com/pievos101/GNN-SubNet ). We have repeated the 80–20% train-test split 20 times and report on the min, median, and max accuracy (see Supplementary Table 1 ; Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

Multi-omics disease module detection with an explainable Greedy Decision Forest

Pfeifer

Baniecki

Saranti

et al. 2022

Sci Rep

Self Cite

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Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a 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 proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.

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Section: Resultsmentioning

confidence: 99%

Multi-omics disease module detection with an explainable Greedy Decision Forest

Pfeifer

Baniecki

Saranti

et al. 2022

Sci Rep

Self Cite

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“…The investigation of XAI has been associated with a growing number of applications in biological research, such as the prediction of proteomic networks for individual patients ( 19 ) and the identification of molecular network modules associated to specific disease phenotypes ( 17 , 18 , 57 ). The use of XAI for the prediction of single-cell gene regulatory networks offers promising opportunities, but several difficulties still need to be considered.…”

Section: Discussionmentioning

confidence: 99%

Single-cell gene regulatory network prediction by explainable AI

Keyl

Bischoff

Dernbach

et al. 2023

Nucleic Acids Research

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The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

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“…GNN models have been designed for analysis of multi-omics pan-cancer data such as gene expression profile, DNA methylation, gene mutation rates, copy number variation, exon expression, and clinical data, with an emphasis on predicting various types of cancers [25]. Pfeifer et al [126] introduced a unique explainable GNN-based framework for cancer subnetwork discovery. The Protein-Protein Interaction (PPI) network topology of each patient is employed, where the nodes are enriched with multiomics data from DNA methylation and gene expression.…”

Section: Graph Neural Network (Gnn)mentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

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GNN-SubNet: disease subnetwork detection with explainable graph neural networks

Cited by 34 publications

References 24 publications

Multi-omics disease module detection with an explainable Greedy Decision Forest

Multi-omics disease module detection with an explainable Greedy Decision Forest

Single-cell gene regulatory network prediction by explainable AI

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

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