TY - BOOK A1 - Wendler, Tilo A1 - Gröttrup, Sören T1 - Data Mining with SPSS Modeler BT - Theory, Exercises and Solutions UR - https://doi.org/10.1007/978-3-319-28709-6 KW - 62-07, 62Hxx, 62Pxx, 62-01 KW - IBM SPSS Modeler KW - data mining KW - mathematical software KW - predictive analytics KW - statistical methods KW - exercises and solutions Y1 - 2016 UR - https://doi.org/10.1007/978-3-319-28709-6 SN - 978-3-319-28709-6 PB - Springer CY - Cham ER - TY - JOUR A1 - Alsmeyer, Gerold A1 - Gröttrup, Sören T1 - Branching within branching: A model for host–parasite co-evolution JF - Stochastic Processes and their Applications UR - https://doi.org/10.1016/j.spa.2015.12.007 KW - Host–parasite co-evolution KW - Branching within branching KW - Galton–Watson process KW - Random environment KW - Infinite random cell line KW - Random tree KW - Extinction probability KW - Extinction–explosion principle KW - Size-biasing KW - Heyde–Seneta norming Y1 - 2015 UR - https://doi.org/10.1016/j.spa.2015.12.007 VL - 126 IS - 6 SP - 1839 EP - 1883 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Alsmeyer, Gerold A1 - Gröttrup, Sören T1 - A host-parasite model for a two-type cell population JF - Advances in Applied Probability UR - https://doi.org/10.1239/aap/1377868536 KW - cell division KW - branching within branching KW - branching process in a random environment KW - host-parasite model KW - extinction characteristics KW - limit theorem Y1 - 2013 UR - https://doi.org/10.1239/aap/1377868536 SN - 1475-6064 SN - 0001-8678 VL - 45 IS - 3 SP - 719 EP - 741 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Hebestreit, Katja A1 - Gröttrup, Sören A1 - Emden, Daniel A1 - Veerkamp, Jannis A1 - Ruckert, Christian A1 - Klein, Hans-Ulrich A1 - Müller-Tidow, Carsten A1 - Dugas, Martin T1 - Leukemia Gene Atlas – A Public Platform for Integrative Exploration of Genome-Wide Molecular Data JF - PLOS ONE N2 - Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org. UR - https://doi.org/10.1371/journal.pone.0039148 Y1 - 2012 UR - https://doi.org/10.1371/journal.pone.0039148 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31698 SN - 1932-6203 VL - 7 IS - 6 PB - PLOS CY - San Francisco ER - TY - INPR A1 - Alsmeyer, Gerold A1 - Gröttrup, Sören T1 - Branching within branching I: The extinction problem UR - https://doi.org/10.48550/arXiv.1505.03979 Y1 - 2015 UR - https://doi.org/10.48550/arXiv.1505.03979 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Alsmeyer, Gerold A1 - Gröttrup, Sören T1 - Branching within branching II: Limit theorems UR - https://doi.org/10.48550/arXiv.1505.03981 Y1 - 2015 UR - https://doi.org/10.48550/arXiv.1505.03981 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Hartung, Kai A1 - Herygers, Aaricia A1 - Kurlekar, Shubham Vijay A1 - Zakaria, Khabbab A1 - Volkan, Taylan A1 - Gröttrup, Sören A1 - Georges, Munir ED - Ekštein, Kamil ED - Pártl, František ED - Konopík, Miloslav T1 - Measuring Sentiment Bias in Machine Translation T2 - Text, Speech, and Dialogue: 26th International Conference: Proceedings UR - https://doi.org/10.1007/978-3-031-40498-6_8 KW - Machine translation KW - sentiment classification KW - bias Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-40498-6_8 SN - 978-3-031-40498-6 SN - 1611-3349 SP - 82 EP - 93 PB - Springer CY - Cham ER - TY - INPR A1 - Pagonis, Panagiotis A1 - Hartung, Kai A1 - Wu, Di A1 - Georges, Munir A1 - Gröttrup, Sören T1 - Analysis of Knowledge Tracing performance on synthesised student data N2 - Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data. UR - https://doi.org/10.48550/arXiv.2401.16832 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2401.16832 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44771 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4535 PB - arXiv CY - Ithaca ER - TY - BOOK A1 - Wendler, Tilo A1 - Gröttrup, Sören T1 - Data Mining with SPSS Modeler BT - Theory, Exercises and Solutions UR - https://doi.org/10.1007/978-3-030-54338-9 Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-54338-9 SN - 978-3-030-54338-9 SN - 978-3-030-54339-6 SN - 978-3-030-54337-2 PB - Springer CY - Cham ET - 2nd edition ER - TY - CHAP A1 - Hartung, Kai A1 - Jäger, Gerhard A1 - Gröttrup, Sören A1 - Georges, Munir ED - Vylomova, Ekaterina ED - Ponti, Edoardo ED - Cotterell, Ryan T1 - Typological Word Order Correlations with Logistic Brownian Motion T2 - Proceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022) N2 - In this study we address the question to what extent syntactic word-order traits of different languages have evolved under correlation and whether such dependencies can be found universally across all languages or restricted to specific language families.To do so, we use logistic Brownian Motion under a Bayesian framework to model the trait evolution for 768 languages from 34 language families. We test for trait correlations both in single families and universally over all families. Separate models reveal no universal correlation patterns and Bayes Factor analysis of models over all covered families also strongly indicate lineage specific correlation patters instead of universal dependencies. KW - Typology KW - Brownian Motion Y1 - 2022 UR - https://aclanthology.org/2022.sigtyp-1.3 SN - 978-1-955917-93-3 SP - 22 EP - 26 PB - Association for Computational Linguistics CY - Stroudsburg ER - TY - CHAP A1 - Pagonis, Panagiotis A1 - Hartung, Kai A1 - Wu, Di A1 - Georges, Munir A1 - Gröttrup, Sören T1 - Analysis of Knowledge Tracing performance on synthesised student data Y1 - 2024 UR - https://sme.uni-bamberg.de/ai4ai/ N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4477 PB - Universität Bamberg CY - Bamberg ER -