@article{WeidnerSchwabWerleetal.2022, author = {Weidner, Felix M. and Schwab, Julian D. and Werle, Silke D. and Ikonomi, Nensi and Lausser, Ludwig and Kestler, Hans A.}, title = {Response to the letter to the editor: On the feasibility of dynamical analysis of network models of biochemical regulation}, volume = {38}, journal = {Bioinformatics}, number = {14}, publisher = {Oxford University Press}, address = {Oxford}, issn = {1367-4811}, doi = {https://doi.org/10.1093/bioinformatics/btac318}, pages = {3676}, year = {2022}, language = {en} } @article{KubisSchwabWerleetal.2018, author = {Kubis, Lea and Schwab, Julian D. and Werle, Silke D. and Lausser, Ludwig and T{\"u}mpel, Stefan and Pfister, Astrid S. and K{\"u}hl, Michael and Kestler, Hans A.}, title = {A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells}, volume = {13}, pages = {e0195126}, journal = {PLOS ONE}, number = {3}, publisher = {PLOS}, address = {San Francisco}, issn = {1932-6203}, doi = {https://doi.org/10.1371/journal.pone.0195126}, year = {2018}, abstract = {Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.}, language = {en} } @article{HuehneKesslerFuerstbergeretal.2018, author = {H{\"u}hne, Rolf and Kessler, Viktor and F{\"u}rstberger, Axel and Werle, Silke D. and Platzer, Matthias and S{\"u}hnel, J{\"u}rgen and Lausser, Ludwig and Kestler, Hans A.}, title = {3D Network exploration and visualisation for lifespan data}, volume = {19 (2018)}, pages = {390}, journal = {BMC Bioinformatics}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {https://doi.org/10.1186/s12859-018-2393-x}, year = {2018}, abstract = {Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.}, language = {en} } @article{LausserSchaeferWerleetal.2020, author = {Lausser, Ludwig and Sch{\"a}fer, Lisa M. and Werle, Silke D. and Kestler, Angelika M. R. and Kestler, Hans A.}, title = {Detecting Ordinal Subcascades}, volume = {52}, journal = {Neural Processing Letters}, number = {3}, publisher = {Springer Science+Business Media}, address = {Dordrecht}, issn = {1573-773X}, doi = {https://doi.org/10.1007/s11063-020-10362-0}, pages = {2583 -- 2605}, year = {2020}, abstract = {Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.}, language = {en} } @article{SeufferleinLausserSteinetal.2024, author = {Seufferlein, Thomas and Lausser, Ludwig and Stein, Alexander and Arnold, Dirk and Prager, Gerald and Kasper-Virchow, Stefan and Niedermeier, Michael and M{\"u}ller, Lothar and Kubicka, Stefan and K{\"o}nig, Alexander and B{\"u}chner-Steudel, Petra and Wille, Kai and Berger, Andreas W. and Kestler, Angelika M. R. and Kraus, Johann M. and Werle, Silke D. and Perkhofer, Lukas and Ettrich, Thomas J. and Kestler, Hans A.}, title = {Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial}, volume = {19}, pages = {e0304324}, journal = {PLOS ONE}, number = {6}, publisher = {PLOS}, address = {San Francisco}, issn = {1932-6203}, doi = {https://doi.org/10.1371/journal.pone.0304324}, year = {2024}, abstract = {Background Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Patients and methods 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2\% accuracy, 71.8\% sensitivity, and 82.5\% specificity. Conclusions We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.}, language = {en} }