TY - JOUR A1 - Nordmeyer, Sarah A1 - Kraus, Milena A1 - Ziehm, Matthias A1 - Kirchner, Marieluise A1 - Schafstedde, Marie A1 - Kelm, Marcus A1 - Niquet, Sylvia A1 - Stephen, Mariet Mathew A1 - Baczko, Istvan A1 - Knosalla, Christoph A1 - Schapranow, Matthieu-Patrick A1 - Dittmar, Gunnar A1 - Gotthardt, Michael A1 - Falcke, Martin A1 - Regitz-Zagrosek, Vera A1 - Kuehne, Titus A1 - Mertins, Philipp T1 - Disease- and sex-specific differences in patients with heart valve disease BT - a proteome study JF - Life Science Alliance N2 - Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease-and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies. Y1 - 2023 U6 - https://doi.org/10.26508/lsa.202201411 SN - 2575-1077 VL - 6 IS - 3 PB - EMBO Press CY - Heidelberg ER - TY - JOUR A1 - Freitas da Cruz, Harry A1 - Pfahringer, Boris A1 - Martensen, Tom A1 - Schneider, Frederic A1 - Meyer, Alexander A1 - Böttinger, Erwin A1 - Schapranow, Matthieu-Patrick T1 - Using interpretability approaches to update "black-box" clinical prediction models BT - an external validation study in nephrology JF - Artificial intelligence in medicine : AIM N2 - Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies. KW - Clinical predictive modeling KW - Nephrology KW - Validation KW - Interpretability KW - methods Y1 - 2021 U6 - https://doi.org/10.1016/j.artmed.2020.101982 SN - 0933-3657 SN - 1873-2860 VL - 111 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Borchert, Florian A1 - Mock, Andreas A1 - Tomczak, Aurelie A1 - Hügel, Jonas A1 - Alkarkoukly, Samer A1 - Knurr, Alexander A1 - Volckmar, Anna-Lena A1 - Stenzinger, Albrecht A1 - Schirmacher, Peter A1 - Debus, Jürgen A1 - Jäger, Dirk A1 - Longerich, Thomas A1 - Fröhling, Stefan A1 - Eils, Roland A1 - Bougatf, Nina A1 - Sax, Ulrich A1 - Schapranow, Matthieu-Patrick T1 - Correction to: Knowledge bases and software support for variant interpretation in precision oncology JF - Briefings in bioinformatics Y1 - 2021 U6 - https://doi.org/10.1093/bib/bbab246 SN - 1467-5463 SN - 1477-4054 VL - 22 IS - 6 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Borchert, Florian A1 - Mock, Andreas A1 - Tomczak, Aurelie A1 - Hügel, Jonas A1 - Alkarkoukly, Samer A1 - Knurr, Alexander A1 - Volckmar, Anna-Lena A1 - Stenzinger, Albrecht A1 - Schirmacher, Peter A1 - Debus, Jürgen A1 - Jäger, Dirk A1 - Longerich, Thomas A1 - Fröhling, Stefan A1 - Eils, Roland A1 - Bougatf, Nina A1 - Sax, Ulrich A1 - Schapranow, Matthieu-Patrick T1 - Knowledge bases and software support for variant interpretation in precision oncology JF - Briefings in bioinformatics N2 - Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process. KW - HiGHmed KW - personalized medicine KW - molecular tumor board KW - data integration KW - cancer therapy Y1 - 2021 U6 - https://doi.org/10.1093/bib/bbab134 SN - 1467-5463 SN - 1477-4054 VL - 22 IS - 6 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Kraus, Sara Milena A1 - Mathew-Stephen, Mariet A1 - Schapranow, Matthieu-Patrick T1 - Eatomics BT - Shiny exploration of quantitative proteomics data JF - Journal of proteome research N2 - Quantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics. KW - R Shiny KW - application KW - label-free KW - proteomics KW - analysis KW - differential KW - abundance KW - experimental design Y1 - 2021 U6 - https://doi.org/10.1021/acs.jproteome.0c00398 SN - 1535-3893 SN - 1535-3907 VL - 20 IS - 1 SP - 1070 EP - 1078 PB - American Chemical Society CY - Washington ER -