TY - JOUR A1 - Peppert, Felix A1 - von Kleist, Max A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks JF - IEEE Transactions on Neural Networks and Learning Systems N2 - Deep convolutional neural networks (DCNNs) are routinely used for image segmentation of biomedical data sets to obtain quantitative measurements of cellular structures like tissues. These cellular structures often contain gaps in their boundaries, leading to poor segmentation performance when using DCNNs like the U-Net. The gaps can usually be corrected by post-hoc computer vision (CV) steps, which are specific to the data set and require a disproportionate amount of work. As DCNNs are Universal Function Approximators, it is conceivable that the corrections should be obsolete by selecting the appropriate architecture for the DCNN. In this article, we present a novel theoretical framework for the gap-filling problem in DCNNs that allows the selection of architecture to circumvent the CV steps. Combining information-theoretic measures of the data set with a fundamental property of DCNNs, the size of their receptive field, allows us to formulate statements about the solvability of the gap-filling problem independent of the specifics of model training. In particular, we obtain mathematical proof showing that the maximum proficiency of filling a gap by a DCNN is achieved if its receptive field is larger than the gap length. We then demonstrate the consequence of this result using numerical experiments on a synthetic and real data set and compare the gap-filling ability of the ubiquitous U-Net architecture with variable depths. Our code is available at https://github.com/ai-biology/dcnn-gap-filling. Y1 - 2022 U6 - https://doi.org/10.1109/TNNLS.2021.3072746 VL - 33 IS - 11 SP - 6194 EP - 6205 ER - TY - JOUR A1 - Birk, Ralph A1 - Raharinirina, N. Alexia A1 - Fackeldey, Konstantin A1 - Richter, Tonio Sebastian A1 - Weber, Marcus T1 - Inferring cultural and social processes based on patterns of statistical relationships between Synodal texts N2 - In this paper, we explore the relationship patterns between Ancient Egyptian texts of the corpus ``Synodal decrees'', which are originating between 243 and 185 BCE, during the Ptolemaic period. Particularly, we are interested in analyzing the grammatical features of the different texts. Conventional data analysis methods such as correspondence Analysis are very useful to explore the patterns of statistical interdependence between categories of variables. However, it is based on a PCA-like dimension-reduction method and turned out to be unsuitable for our dataset due to the high dimensionality of our data representations. Additionally, the similarity between pairs of texts and pairs of grammatical features is observed through the distance between their representation, but the degree of association between a particular grammatical feature and a text is not. Here, we applied a qualitative Euclidean embedding method that provides a new Euclidean representation of the categories of variables. This new representation of the categories is constructed in such a way that all the patterns of statistical interdependence, similarity, and association, are seen through the Euclidean distance between them. Nevertheless, the PCA-like dimension-reduction method also performed poorly on our new representation. Therefore, we obtained a two-dimensional visualization using non-linear methods such UMAP or t-SNE. Although these dimension-reduction methods reduced the interpretability of interpoint distances, we were still able to identify important similarity patterns between the Synodal text as well as their association patterns with the grammatical features. Y1 - 2021 ER - TY - JOUR A1 - Raharinirina, Nomenjanahary Alexia A1 - Sunkara, Vikram A1 - von Kleist, Max A1 - Fackeldey, Konstantin A1 - Weber, Marcus T1 - Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables JF - PLOS ONE Y1 - 2024 U6 - https://doi.org/10.1371/journal.pone.0302425 VL - 19 IS - 5 PB - Public Library of Science ER - TY - JOUR A1 - Vu-Han, Tu-Lan A1 - Schettino, Rodrigo Bermudez A1 - Weiß, Claudia A1 - Perka, Carsten A1 - Winkler, Tobias A1 - Sunkara, Vikram A1 - Pumberger, Matthias T1 - An interpretable data-driven prediction model to anticipate scoliosis in spinal muscular atrophy in the era of (gene-) therapies JF - Scientific Reports N2 - 5q-spinal muscular atrophy (SMA) is a neuromuscular disorder (NMD) that has become one of the first 5% treatable rare diseases. The efficacy of new SMA therapies is creating a dynamic SMA patient landscape, where disease progression and scoliosis development play a central role, however, remain difficult to anticipate. New approaches to anticipate disease progression and associated sequelae will be needed to continuously provide these patients the best standard of care. Here we developed an interpretable machine learning (ML) model that can function as an assistive tool in the anticipation of SMA-associated scoliosis based on disease progression markers. We collected longitudinal data from 86 genetically confirmed SMA patients. We selected six features routinely assessed over time to train a random forest classifier. The model achieved a mean accuracy of 0.77 (SD 0.2) and an average ROC AUC of 0.85 (SD 0.17). For class 1 ‘scoliosis’ the average precision was 0.84 (SD 0.11), recall 0.89 (SD 0.22), F1-score of 0.85 (SD 0.17), respectively. Our trained model could predict scoliosis using selected disease progression markers and was consistent with the radiological measurements. During post validation, the model could predict scoliosis in patients who were unseen during training. We also demonstrate that rare disease data sets can be wrangled to build predictive ML models. Interpretable ML models can function as assistive tools in a changing disease landscape and have the potential to democratize expertise that is otherwise clustered at specialized centers. Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-62720-w VL - 14 IS - 11838 ER - TY - JOUR A1 - Kostré, Margarita A1 - Djurdjevac Conrad, Natasa A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Exploration of Particle Swarm Optimisation Algorithm with Divergent Parameters JF - Natural Computing Y1 - 2024 ER - TY - JOUR A1 - Vu‐Han, Tu‐Lan A1 - Sunkara, Vikram A1 - Bermudez‐Schettino, Rodrigo A1 - Schwechten, Jakob A1 - Runge, Robin A1 - Perka, Carsten A1 - Winkler, Tobias A1 - Pokutta, Sebastian A1 - Weiß, Claudia A1 - Pumberger, Matthias T1 - Feature Engineering for the Prediction of Scoliosis in 5q‐Spinal Muscular Atrophy JF - Journal of Cachexia, Sarcopenia and Muscle Y1 - 2025 U6 - https://doi.org/10.1002/jcsm.13599 VL - 16 IS - 1 SP - e13599 ER - TY - JOUR A1 - Regényi, Enikő A1 - Mashreghi, Mir-Farzin A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Exploring transcription modalities from bimodal, single-cell RNA sequencing data JF - NAR Genomics and Bioinformatics N2 - Abstract There is a growing interest in generating bimodal, single-cell RNA sequencing (RNA-seq) data for studying biological pathways. These data are predominantly utilized in understanding phenotypic trajectories using RNA velocities; however, the shape information encoded in the two-dimensional resolution of such data is not yet exploited. In this paper, we present an elliptical parametrization of two-dimensional RNA-seq data, from which we derived statistics that reveal four different modalities. These modalities can be interpreted as manifestations of the changes in the rates of splicing, transcription or degradation. We performed our analysis on a cell cycle and a colorectal cancer dataset. In both datasets, we found genes that are not picked up by differential gene expression analysis (DGEA), and are consequently unnoticed, yet visibly delineate phenotypes. This indicates that, in addition to DGEA, searching for genes that exhibit the discovered modalities could aid recovering genes that set phenotypes apart. For communities studying biomarkers and cellular phenotyping, the modalities present in bimodal RNA-seq data broaden the search space of genes, and furthermore, allow for incorporating cellular RNA processing into regulatory analyses. Y1 - 2024 U6 - https://doi.org/10.1093/nargab/lqae179 SN - 2631-9268 VL - 6 IS - 4 PB - Oxford University Press (OUP) ER - TY - CHAP A1 - Chaukair, Mustafa A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - On the Activation Space of ReLU Equipped Deep Neural Networks T2 - Procedia Computer Science N2 - Modern Deep Neural Networks are getting wider and deeper in their architecture design. However, with an increasing number of parameters the decision mechanisms becomes more opaque. Therefore, there is a need for understanding the structures arising in the hidden layers of deep neural networks. In this work, we present a new mathematical framework for describing the canonical polyhedral decomposition in the input space, and in addition, we introduce the notions of collapsing- and preserving patches, pertinent to understanding the forward map and the activation space they induce. The activation space can be seen as the output of a layer and, in the particular case of ReLU activations, we prove that this output has the structure of a polyhedral complex. Y1 - 2023 U6 - https://doi.org/10.1016/j.procs.2023.08.200 VL - 222 SP - 624 EP - 635 ER - TY - JOUR A1 - Coomber, Celvic A1 - Chewle, Surahit A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Investigating Endogenous Opioids Unravels the Mechanisms Behind Opioid-Induced Constipation, a Mathematical Modeling Approach JF - International Journal of Molecular Sciences N2 - Endogenous opioids, such as Endomorphin-2, are not typically associated with severe constipation, unlike pharmaceutical opioids, which induce opioid-induced constipation (OIC) by activating μ-opioid receptors in the gastrointestinal tract. In this study, we present a mathematical model, which integrates the serotonergic and opioid pathways, simulating the interaction between serotonin and opioid signaling within the enteric nervous system (ENS). The model explores the mechanisms underlying OIC, with a focus on the change in adenylyl cyclase (AC) activity, cAMP accumulation, and the distinct functionalities of Endomorphin-2 compared to commonly used pharmaceutical opioids. We study the effects of Morphine, Fentanyl, and Methadone and contrast them with Endomorphin-2. Our findings reveal that opioids do not perturb the signaling of serotonin, but only the activity of AC, suggesting that serotonin levels have no influence on improving opioid-induced constipation. Furthermore, this study reveals that the primary difference between endogenous and pharmaceutical opioids is their degradation rates. This finding shows that modulating opioid degradation rates significantly improves cAMP recovery. In conclusion, our insights steer towards exploring opioid degrading enzymes, localized to the gut, as a strategy for mitigating OIC. Y1 - 2025 U6 - https://doi.org/10.3390/ijms26136207 VL - 26 IS - 13 ER - TY - JOUR A1 - Raharinirina, Alexia N. A1 - Peppert, Felix A1 - von Kleist, Max A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments JF - Patterns N2 - Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealized due to technical and computational challenges. In this work we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: data pre-processing, feature extraction, and inference. We observed that the regulatory signature is captured in the statistical moments of scRNA-seq data and requires computationally intensive minimization solvers to extract it. Furthermore, current data pre-processing might not conserve these statistical moments. Although our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking—finding computationally feasible multi-dimensional statistics of data—is imperative for designing GRN inference methods. Y1 - 2021 U6 - https://doi.org/10.1016/j.patter.2021.100332 VL - 2 IS - 9 ER - TY - JOUR A1 - Sunkara, Vikram A1 - Heinz, Gitta A. A1 - Heinrich, Frederik F. A1 - Durek, Pawel A1 - Mobasheri, Ali A1 - Mashreghi, Mir-Farzin A1 - Lang, Annemarie T1 - Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint JF - Osteoarthritis and Cartilage Y1 - 2021 U6 - https://doi.org/10.1016/j.joca.2021.03.007 VL - 29 IS - 6 SP - 905 EP - 914 ER - TY - JOUR A1 - Sunkara, Vikram A1 - Heinz, Gitta A. A1 - Heinrich, Frederik F. A1 - Durek, Pawel A1 - Mobasheri, Ali A1 - Mashreghi, Mir-Farzin A1 - Lang, Annemarie T1 - Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint JF - bioarxiv (Accepted in Osteoarthr. Cartil.) Y1 - 2020 U6 - https://doi.org/10.1101/2020.06.13.148056 ER - TY - JOUR A1 - Lang, Annemarie A1 - Helfmeier, Sarah A1 - Stefanowski, Jonathan A1 - Kuppe, Aditi A1 - Sunkara, Vikram A1 - Pfeiffenberger, Moritz A1 - Wolter, Angelique A1 - Damerau, Alexandra A1 - Hemmati-Sadeghi, Shabnam A1 - Ringe, Jochen A1 - Haag, Rainer A1 - Hauser, Anja E. A1 - Löhning, Max A1 - Perka, Carsten A1 - Duda, Georg A1 - Hoff, Paula A1 - Schmidt-Bleek, Katharina A1 - Gaber, Timo A1 - Buttgereit, Frank T1 - HIF-stabilization prevents delayed fracture healing JF - bioarxiv Y1 - 2020 U6 - https://doi.org/10.1101/2020.07.02.182832 ER - TY - JOUR A1 - Laydon, Daniel J. A1 - Sunkara, Vikram A1 - Boelen, Lies A1 - Bangham, Charles R. M. A1 - Asquith, Becca T1 - The relative contributions of infectious and mitotic spread to HTLV-1 persistence JF - PLOS Computational Biology Y1 - 2020 U6 - https://doi.org/10.1371/journal.pcbi.1007470 ER - TY - JOUR A1 - Sunkara, Vikram A1 - Lewis, Angus A1 - Nguyen, Giang T. A1 - O'Reilly, Malgorzata M. A1 - Bean, Nigel T1 - A discontinuous Galerkin method for approximating the stationary distribution of stochastic fluid-fluid processes JF - Methodology and Computing in Applied Probability Y1 - 2022 U6 - https://doi.org/10.1007/s11009-022-09945-2 ER - TY - JOUR A1 - Lang, Annemarie A1 - Vester, Antja A1 - Ferreira-Gomes, M. A1 - Guerra, G.M. A1 - Heinrich, Fredrich R. A1 - Götzke, C.C. A1 - Kurmies, Sebastian A1 - Sunkara, Vikram A1 - Durek, Pawel A1 - Boerckel, Joel D. A1 - Mashreghi, Mir-Farzin T1 - PIPELINE FOR SINGLE CELL SEQUENCING OF HUMAN CHONDROCYTE PELLET CULTURES TO DELINEATE IL-1β MODULATED CHANGES IN CELL HETEROGENEITY JF - Osteoarthritis and Cartilage Y1 - 2022 U6 - https://doi.org/10.1016/j.joca.2022.02.112 VL - 30 IS - Supplement 1 SP - S90 ER - TY - JOUR A1 - Schulze, Kenrick A1 - Peppert, Felix A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Chimeric U-Net – Modifying the standard U-Net towards Explainability JF - Artificial Intelligence N2 - Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net , encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under: https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch Y1 - 2025 U6 - https://doi.org/10.1016/j.artint.2024.104240 VL - 338 ER - TY - JOUR A1 - Kostré, Margarita A1 - Sunkara, Vikram A1 - Schütte, Christof A1 - Djurdjevac Conrad, Natasa T1 - Understanding the Romanization Spreading on Historical Interregional Networks in Northern Tunisia JF - Applied Network Science N2 - Spreading processes are important drivers of change in social systems. To understand the mechanisms of spreading it is fundamental to have information about the underlying contact network and the dynamical parameters of the process. However, in many real-wold examples, this information is not known and needs to be inferred from data. State-of-the-art spreading inference methods have mostly been applied to modern social systems, as they rely on availability of very detailed data. In this paper we study the inference challenges for historical spreading processes, for which only very fragmented information is available. To cope with this problem, we extend existing network models by formulating a model on a mesoscale with temporal spreading rate. Furthermore, we formulate the respective parameter inference problem for the extended model. We apply our approach to the romanization process of Northern Tunisia, a scarce dataset, and study properties of the inferred time-evolving interregional networks. As a result, we show that (1) optimal solutions consist of very different network structures and spreading rate functions; and that (2) these diverse solutions produce very similar spreading patterns. Finally, we discuss how inferred dominant interregional connections are related to available archaeological traces. Historical networks resulting from our approach can help understanding complex processes of cultural change in ancient times. KW - mesoscale spreading process KW - network inference KW - time-evolving network KW - romanization spreading KW - scarce data Y1 - 2022 U6 - https://doi.org/10.1007/s41109-022-00492-w VL - 7 PB - Springer Nature ER - TY - CHAP A1 - Kostre, Margarita A1 - Sunkara, Vikram A1 - Djurdjevac Conrad, Natasa T1 - Inference of historical influence networks T2 - International Conference on Complex Networks & Their Applications Proceedings N2 - We study the romanization process of northern Africa from 50 BC till 300 AD. Our goal is to infer the communication strength between different subregions, based on the evolution of the status of cities. Herefore, we use the general inverse infection model, that infers the weights of a known underlying network, given observations of the spreading on this network. As infection process we choose the SI metapopulation model, where I stands for a city with a Roman status. To solve the minimization problem we use the particle swarm optimization algorithm with a specific choice of parameters. Y1 - 2021 U6 - https://doi.org/10.12752/8558 SP - 110 EP - 113 ET - International Conference on Complex Networks & Their Applications ER - TY - GEN A1 - Kostré, Margarita A1 - Sunkara, Vikram A1 - Schütte, Christof A1 - Djurdjevac Conrad, Nataša T1 - Understanding the Romanization Spreading on Historical Interregional Networks in Northern Tunisia N2 - Spreading processes are important drivers of change in social systems. To understand the mechanisms of spreading it is fundamental to have information about the underlying contact network and the dynamical parameters of the process. However, in many real-wold examples, this information is not known and needs to be inferred from data. State-of-the-art spreading inference methods have mostly been applied to modern social systems, as they rely on availability of very detailed data. In this paper we study the inference challenges for historical spreading processes, for which only very fragmented information is available. To cope with this problem, we extend existing network models by formulating a model on a mesoscale with temporal spreading rate. Furthermore, we formulate the respective parameter inference problem for the extended model. We apply our approach to the romanization process of Northern Tunisia, a scarce dataset, and study properties of the inferred time-evolving interregional networks. As a result, we show that (1) optimal solutions consist of very different network structures and spreading rate functions; and that (2) these diverse solutions produce very similar spreading patterns. Finally, we discuss how inferred dominant interregional connections are related to available archaeological traces. Historical networks resulting from our approach can help understanding complex processes of cultural change in ancient times. T3 - ZIB-Report - 22-10 KW - mesoscale spreading process, network inference, time-evolving network, romanization spreading, scarce data Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-86764 SN - 1438-0064 ER -