Biology subjects

Hauschild, A.-C.

Publications and source records attributed to Hauschild, A.-C..

4 recordsLinked to original sources

bioRxiv2023

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

Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).

bioinformatics↗

bioRxiv2022

Interactive explainable AI platform for graph neural networks

BackgroundLack of trust in artificial intelligence (AI) models in medicine is still the key blockage for the use of AI in clinical decision support systems (CDSS). Although AI models are already performing excellently in systems medicine, their black-box nature entails that patient-specific decisions are incomprehensible for the physician. This is especially true for very complex models such as graph neural networks (GNNs), a common state-of-the-art approach to model biological networks such as protein-protein-interaction graphs (PPIs) to predict clinical outcomes. The aim of explainable AI (XAI) algorithms is to "explain" to a human domain expert, which input features, such as genes, influenced a specific recommendation. However, in the clinical domain, it is essential that these explanations lead to some degree of causal understanding by a clinician in the context of a specific application. ResultsWe developed the CLARUS platform, aiming to promote human understanding of GNN predictions by allowing the domain expert to validate and improve the decision-making process. CLARUS enables the visualisation of the patient-specific biological networks used to train and test the GNN model, where nodes and edges correspond to gene products and their interactions, for instance. XAI methods, such as GNNExplainer, compute relevance values for genes and interactions. The CLARUS graph visualisation highlights gene and interaction relevances by color intensity and line thickness, respectively. This enables domain experts to gain deeper insights into the biological network by identifying the most influential sub-graphs and molecular pathways crucial for the decision-making process. More importantly, the expert can interactively alter the patient-specific PPI network based on the acquired understanding and initiate re-prediction or retraining. This interactivity allows to ask manual counterfactual questions and analyse the resulting effects on the GNN prediction. ConclusionTo the best of our knowledge, we present the first interactive XAI platform prototype, CLARUS, that allows not only the evaluation of specific human counterfactual questions based on user-defined alterations of patient PPI networks and a re-prediction of the clinical outcome but also a retraining of the entire GNN after changing the underlying graph structures. The platform is currently hosted by the GWDG on https://rshiny.gwdg.de/apps/clarus/.

bioinformatics↗

bioRxiv2022

On the importance of data transformation for data integration in single-cell RNA sequencing analysis

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in single-cell RNA (scRNA) sequencing have opened a multitude of possibilities to study tissues down to the level of cellular populations. Subsequently, this enabled various scRNA studies that reported novel or previously undetected subpopulations and their functions by integrating multiple datasets. However, the heterogeneity in single-cell sequencing data makes it unfeasible to adequately integrate multiple datasets generated from different studies. This heterogeneity originates from various sources of noise due to technological limitations. Thus, particular procedures are required to adjust such effects prior to further integrative analysis. Over the last years, numerous single-cell data analysis workflows have been introduced, implementing various read-count transformation methods for de-noising and batch correction. A detailed review of recent single-cell studies shows while many analysis procedures employ various preprocessing steps, they often neglect the importance of a well-chosen and optimized data transformation. This fact is particularly alarming since these data transformations can alter data distribution and thus have a crucial impact on subsequent downstream cell clustering results. Therefore, this study investigates the effects of the various data transformation methods on three different public data scenarios and evaluates them with the most commonly used dimensionality reduction and clustering analysis. Additionally, we discuss its implications for the subsequent application of different deep neural network approaches, such as auto encoders and transfer learning. In summary, our benchmark analysis shows that a large portion of batch effects and noise can be mitigated by simple but well-chosen data transformation methods. We conclude that such optimized preprocessing is crucial and should be the baseline for all comparative single-cell sequencing studies, particularely for integrative analysis of multiple data sets.

bioinformatics↗

bioRxiv2021

Transfer Learning Compensates Limited Data, Batch-Effects, And Technical Heterogeneity In Single-Cell Sequencing

AO_SCPLOWBSTRACTC_SCPLOWTremendous advances in next-generation sequencing technology have enabled the accumulation of large amounts of omics data in various research areas over the past decade. However, study limitations due to small sample sizes, especially in rare disease clinical research, technological heterogeneity, and batch effects limit the applicability of traditional statistics and machine learning analysis. Here, we present a meta-learning approach to transfer knowledge from big data and reduce the search space in data with small sample sizes. Few-shot learning algorithms integrate meta-learning to overcome data scarcity and data heterogeneity by transferring molecular pattern recognition models from datasets of unrelated domains. We explore few-shot learning models with large scale public dataset, TCGA (The Cancer Genome Atlas) and GTEx dataset, and demonstrate their potential as meta-learning dataset in other molecular pattern recognition tasks. Our results show that transfer learning is very effective for datasets with a limited sample size. Furthermore, we show that our approach can transfer knowledge across technological heterogeneity, e.g., from bulk cell to single-cell data. Our approach can overcome study size constraints, batch effects, and technological limitations in analyzing single-cell data by leveraging existing bulk-cell sequencing data.

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