Lucas Plagwitz

Background: Chromatin organization plays a crucial role in gene regulation and is associated with various severe diseases like cancer. Since chromatin changes are potentially reversible, a deeper understanding of the alterations could be harnessed for the development of new therapies. Circular Chromosome Conformation Capture Sequencing (4C-seq) is a sequencing technique enabling the identification of chromatin interactions between genes and regulatory elements. This work aims to develop an ensemble algorithm that utilizes synergies among available 4C-seq tools, which in turn allows to achieve improved 4C-seq chromatin interaction calling. We employed existing 4C-seq algorithms using a weighted-voting approach. By optimizing the tool weights according to various predictive performance metrics using gradient-free optimization strategies, we demonstrate the potential of combining multiple 4C-seq analysis tools for interaction calling. Results: Our results demonstrate that a weighted-voting-based ensemble approach significantly improves predictive performance in chromatin interaction detection in a leave-one-group-out cross-validation setting, achieving a mean F1-score of 0.31 and a mean AUPRC of 0.34, compared to 0.13 and 0.16, respectively. To make this approach accessible, we integrated it into fourSynergy, a 4C-seq analysis framework focusing on near-bait 4C-seq interactions that includes a Snakemake pipeline, an R/Bioconductor package, and an interactive Shiny application. Conclusions: This work provides not only a comprehensive curated collection of 4C-seq datasets, but also demonstrates that ensemble approaches can improve predictive performance in chromatin interaction detection compared to individual 4C-seq algorithms.

Motivation: Chromatin organization plays a crucial role in gene regulation and is associated with various severe diseases like cancer. Since chromatin changes are potentially reversible, a deeper understanding of the alterations needs to be harnessed for the development of new therapies. Circular Chromosome Conformation Capture Sequencing (4C-seq) is a sequencing technique enabling the identification of chromatin interactions between genes and regulatory elements. This work aims to develop an ensemble algorithm that utilizes synergies among available 4C-seq tools, which in turn allows to achieve superior predictive performance in interaction calling. Results: We employed existing 4C-seq algorithms using a weighted-voting approach. By optimizing the tool weights according to various predictive metrics using gradient-free optimization strategies, we demonstrate the potential of combining multiple 4C-seq analysis tools for interaction calling. Our results indicate that a weighted-voting based ensemble approach can outperform individual algorithms in various datasets. Although the optimal solutions differ across the 4C-seq datasets, we successfully identified global solutions that outperform the individual algorithms for all datasets analyzed. Availability: https://github.com/sophiewind/fourSynergy, https://github.com/sophiewind/fourSynergy_pip

Aims: Electrocardiogram (ECG) recordings are fundamental for diagnosing cardiac conditions. Recent advances in automatic ECG analysis have been dominated by deep learning, particularly convolutional neural networks (CNNs). CNNs excel in processing high-dimensional signal data, where their ability to automatically extract complex features has enabled significant progress. However, while CNNs are powerful for biomedical signal analysis, their application to ECG data also carries disadvantages, such as the requirement for large annotated datasets and limited explainability. To address these challenges, we aim to reintroduce shallow learning methods, such as linear classifiers, by leveraging the cyclic nature of ECG signals. Methods and results: We developed an adaptive transformation that restructures ECG signals into a fully structured format suitable for shallow learning algorithms. This method aligns R-peaks across all signals in a dataset and resamples the inter-QRS segments to match a predefined reference heart rate. The approach was systematically evaluated across tasks including classification, clustering, and explainability. Our transformation substantially improved the performance of shallow learning techniques. Compared with CNN approaches, shallow models trained on transformed ECGs achieved superior accuracy and interpretability in data-limited scenarios. Conclusion: We demonstrate that shallow machine learning methods, when combined with our alignment-based transformation, can reach CNN level performance in ECG analysis, especially under conditions of limited training data. This approach offers clear advantages in classification, clustering, and explainability and provides an accessible alternative to deep learning. To facilitate adoption and further research, we release a publicly available framework for ECG signal alignment at https://github.com/imi-ms/rlign.

We thank the author for his constructive commentary on our work describing QTc estimation with QTcNet.¹ We appreciate the recognition of both the external validation strategy and the open-source framework, and we agree that the following points highlight important methodological aspects for translating machine learning-based ECG analysis into clinical practice.

Background: Electrocardiogram (ECG) data constitutes one of the most widely available biosignal data in clinical and research settings, providing critical insights into cardiovascular diseases as well as broader health conditions. Advancements in deep learning demonstrate high performance in diverse ECG classification tasks, ranging from arrhythmia detection to risk prediction for various diseases. However, the widespread adoption of deep learning for ECG analysis faces significant barriers, including the heterogeneity of file formats, restricted access to pretrained model weights, and complex technical workflows for out-of-domain users. Objective: This study aims to address major bottlenecks in ECG-based deep learning by introducing ExChanGeAI, an open-source, web-based platform designed to offer an integrated, user-friendly platform for ECG data analysis. Our objective is to streamline the entire workflow—from initial data ingestion (regardless of device or format) and intuitive visualization to privacy-preserving model training and task-specific fine-tuning—making advanced ECG deep learning accessible for both clinical researchers and practitioners without machine learning (ML) expertise. Methods: ExChanGeAI incorporates robust preprocessing modules for various ECG file types, a set of interactive visualization tools for exploratory data analysis, and multiple state-of-the-art deep learning architectures for ECGs. Users can choose to train models from scratch or fine-tune pretrained models using their own datasets, while all computations are performed locally to ensure data privacy. The platform is adaptable for deployment on personal computers as well as scalable to high-performance computing infrastructures. We demonstrate the platform’s performance on several clinically relevant classification tasks across 3 external and heterogeneous validation datasets, including a newly curated test set from routine care, evaluating both model generalizability and resource efficiency. Results: Our experiments show that de novo training with user-provided, task-specific data can outperform a leading foundation model, while requiring substantially fewer parameters and computational resources. The platform enables users to empirically determine the most suitable model for their specific tasks, based on systematic validations, while lowering technical barriers for out-of-domain experts and promoting open research. Conclusions: ExChanGeAI provides a comprehensive, privacy-aware platform that democratizes access to ECG analysis and model training. By simplifying complex workflows, ExChanGeAI empowers out-of-domain researchers to use state-of-the-art ML on diverse datasets, democratizing the access to ML in the field of ECG data. The platform is available as open-source code under the Massachusetts Institute of Technology (MIT) license.

Aims: Automated QTc measurements from commercial ECG systems often diverge from expert readings. We developed QTcNet, a deep learning model trained and validated on multiple large ECG datasets to improve automated QTc measurement accuracy. Methods and results: QTcNet employs a regression-based convolutional neural network architecture. It was trained on 120 300 algorithm-labelled ECGs (60 150 from an internal hospital cohort and 60 150 from the MIMIC-IV dataset) after correction for a vendor-specific +15 ms bias. Performance was evaluated against expert QTc measurements in three independent datasets: PTB Diagnostic ECG Database (n = 100 ECGs in validation set), QTcMS (n = 210), and ECGRDVQ (n = 5219). The effect of fine-tuning on cardiologist-annotated ECGs was tested in the PTB database (n = 449 in fine-tuning set). Model explainability analyses were performed with Integrated Gradient maps. QTcNet reduced cross-cohort mean absolute error (MAE) from 23.4 to 13.4 ms and root mean square error (RMSE) from 40.1 to 22.1 ms, almost halving large (>50 ms) outliers. Fine-tuning only reduced errors in the PTB dataset but did not improve cross-cohort performance. Integrated Gradient maps confirmed that the model concentrated on QRS onset and T wave offset, supporting physiological plausibility. Conclusion: QTcNet, trained on large-scale algorithmically labelled data, consistently outperformed conventional algorithms across three independent, external validation datasets. Fine-tuning of QTcNet may adapt the model to the characteristics of specific cohorts but reduces external validity in other cohorts. We openly release the full model and code, along with a ready-to-use online implementation at https://qtcnet.uni-muenster.de, facilitating further research and community-driven improvement.

Background and Aims: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusive electrocardiogram (ECG) findings or biomarker results. A deep learning model to detect ECG patterns associated with revascularization likelihood was developed, aiming to guide further assessment and reduce diagnostic uncertainty. Methods: A convolutional neural network model was trained on 144 691 ED visits from a US cohort (60 ± 19 years; 53% female; 0.6% revascularization), tested on a separate test cohort (n = 35 995), and benchmarked against clinician ECG interpretation and cardiac troponin T (TnT). External validation was performed for the outcomes revascularization and Type 1 myocardial infarction (MI) on 18 673 ED visits from Europe (55 ± 21 years; 49% female; 1.5% revascularization; 1% Type 1 MI). Primary performance metric was area under the receiver operating characteristic curve (AUROC). Results: In the test cohort, the model achieved an AUROC of 0.91 (95% confidence interval [CI] 0.91–0.91), outperforming clinician ECG interpretation (AUROC 0.65, 95% CI 0.54–0.76) and conventional cardiac TnT (AUROC 0.71). In the external validation cohort, ECG model AUROC was 0.81 (95% CI 0.81–0.82) for revascularization, and 0.85 (95% CI 0.84–0.85) for Type 1 MI, compared with 0.70 (95% CI 0.57–0.83) and 0.74 (95% CI 0.56–0.92) for clinician interpretation, and 0.85 and 0.87 for high-sensitivity (hs)-TnT, respectively. The ECG model had higher specificity but lower sensitivity compared with high-sensitivity-troponin T. Conclusions: The model was able to detect revascularization and Type 1 MI with competitive performance, suggesting a potential role to complement current clinical assessment.

We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity recognition tasks. Using a German cardiology report dataset and the i2b2 Smoking Challenge dataset, we demonstrate that fine-tuning small LLMs locally on limited training data can improve performance achieving comparable results to larger models. Our experiments show that fine-tuning improves performance on both tasks, with notable gains observed with as few as 200-300 training examples. Overall, the study highlights the potential of task-specific fine-tuning of LLMs for automating clinical workflows and efficiently extracting structured data from unstructured medical text.

Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning methods, providing novel insights into cardiovascular diseases and broader health conditions. However, heterogeneity of electrocardiogram formats, limited access to deep learning model weights and intricate algorithmic steps for effective fine-tuning for own disease target labels result in complex workflows. In this work, we introduce ExChanGeAI, a web-based end-to-end platform that streamlines the reading of different formats, pre-processing, visualization and custom machine learning with local and privacy-preserving fine-tuning. ExChanGeAI is adaptable for use on both personal computers and scalable to high performance server environments. The platform offers state-of-the-art deep learning models for training from scratch, alongside our novel open-source electrocardiogram foundation model CardX, pre-trained on over one million electrocardiograms. Evaluation across three external validation sets, including an entirely new testset extracted from routine care, demonstrate the fine-tuning capabilities of ExChanGeAI. CardX outperformed the benchmark foundation model while requiring significantly fewer parameters and lower computational resources. The platform enables users to empirically determine the most suitable model for their specific tasks based on systematic validations.The code is available at https://imigitlab.uni-muenster.de/published/exchangeai .

Introduction: Large Language Models (LLMs) like ChatGPT have become increasingly prevalent. In medicine, many potential areas arise where LLMs may offer added value. Our research focuses on the use of open-source LLM alternatives like Llama 3, Gemma, Mistral, and Mixtral to extract medical parameters from German clinical texts. We concentrate on German due to an observed gap in research for non-English tasks. Objective: To evaluate the effectiveness of open-source LLMs in extracting medical parameters from German clinical texts, specially focusing on cardiovascular function indicators from cardiac MRI reports. Methods: We extracted 14 cardiovascular function indicators, including left and right ventricular ejection fraction (LV-EF and RV-EF), from 497 variously formulated cardiac magnetic resonance imaging (MRI) reports. Our systematic analysis involved assessing the performance of Llama 3, Gemma, Mistral, and Mixtral models in terms of right annotation and named entity recognition (NER) accuracy. Results: The analysis confirms strong performance with up to 95.4% right annotation and 99.8% NER accuracy across different architectures, despite the fact that these models were not explicitly fine-tuned for data extraction and the German language. Conclusion: The results strongly recommend using open-source LLMs for extracting medical parameters from clinical texts, including those in German, due to their high accuracy and effectiveness even without specific fine-tuning.