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182 results on '"Trautmann, H."'

1. The KMT2A recombinome of acute leukemias in 2023

Author

Meyer, C., Larghero, P., Almeida Lopes, B., Burmeister, T., Gröger, D., Sutton, R., Venn, N. C., Cazzaniga, G., Corral Abascal, L., Tsaur, G., Fechina, L., Emerenciano, M., Pombo-de-Oliveira, M. S., Lund-Aho, T., Lundán, T., Montonen, M., Juvonen, V., Zuna, J., Trka, J., Ballerini, P., Lapillonne, H., Van der Velden, V. H. J., Sonneveld, E., Delabesse, E., de Matos, R. R. C., Silva, M. L. M., Bomken, S., Katsibardi, K., Keernik, M., Grardel, N., Mason, J., Price, R., Kim, J., Eckert, C., Lo Nigro, L., Bueno, C., Menendez, P., zur Stadt, U., Gameiro, P., Sedék, L., Szczepański, T., Bidet, A., Marcu, V., Shichrur, K., Izraeli, S., Madsen, H. O., Schäfer, B. W., Kubetzko, S., Kim, R., Clappier, E., Trautmann, H., Brüggemann, M., Archer, P., Hancock, J., Alten, J., Möricke, A., Stanulla, M., Lentes, J., Bergmann, A. K., Strehl, S., Köhrer, S., Nebral, K., Dworzak, M. N., Haas, O. A., Arfeuille, C., Caye-Eude, A., Cavé, H., and Marschalek, R.

Published
2023
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2. Accurate WiFi-Based Indoor Positioning with Continuous Location Sampling

Author

van Engelen, J. E., van Lier, J. J., Takes, F. W., Trautmann, H., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Brefeld, Ulf, editor, Curry, Edward, editor, Daly, Elizabeth, editor, MacNamee, Brian, editor, Marascu, Alice, editor, Pinelli, Fabio, editor, Berlingerio, Michele, editor, and Hurley, Neil, editor

Published
2019
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5. S106: UBTF-ATXN7L3 GENE FUSION DUE TO 17Q21.31 DELETION DEFINES NOVEL HIGH-RISK ALL SUBTYPE AMENABLE TO MRD-BASED TREATMENT INTENSIFICATION

Author

Bastian, L., primary, Hartmann, A., additional, Beder, T., additional, Hänzelmann, S., additional, Kässens, J., additional, Bultmann, M., additional, Höppner, M. P., additional, Franzenburg, S., additional, Wittig, M., additional, Franke, A., additional, Nagel, I., additional, Spielmann, M., additional, Reimer, N., additional, Busch, H., additional, Schwartz, S., additional, Steffen, B., additional, Viardot, A., additional, Döhner, K., additional, Kondakci, M., additional, Wulf, G., additional, Wendelin, K., additional, Renzelmann, A., additional, Kiani, A., additional, Trautmann, H., additional, Neumann, M., additional, Gökbuget, N., additional, Brüggemann, M., additional, and Baldus, C., additional

Published
2022
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8. Designing Air Flow with Surrogate-assisted Phenotypic Niching

Author

Hagg, A., Wilde, D., Asteroth, A., Bäck, T.H.W., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M.T.M., Trautmann, H., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M.T.M., and Trautmann, H.

Subjects
FOS: Computer and information sciences, 050101 languages & linguistics, Optimization problem, Computer science, 02 engineering and technology, Machine learning, computer.software_genre, Domain (software engineering), Set (abstract data type), Computational Engineering, Finance, and Science (cs.CE), 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Fluid dynamics, FOS: Mathematics, 0501 psychology and cognitive sciences, Mathematics - Numerical Analysis, Neural and Evolutionary Computing (cs.NE), Computer Science - Computational Engineering, Finance, and Science, business.industry, 05 social sciences, Sampling (statistics), Computer Science - Neural and Evolutionary Computing, Numerical Analysis (math.NA), Solver, 020201 artificial intelligence & image processing, Artificial intelligence, Focus (optics), business, computer
Abstract

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we could quickly understand the complex behaviors that can emerge in said domains instead? We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm which allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem. A fast GPU-based fluid dynamics solver is used in conjunction with surrogate models to accurately predict fluid characteristics from the shapes that produce the air flow. We show that these features can be modeled in a data-driven way while sampling to improve performance, rather than explicitly sampling to improve feature models. Our method can reduce the need to run an infeasibly large set of simulations while still being able to design a large diversity of air flows and the shapes that cause them. Discovering diversity of behaviors helps engineers to better understand expensive domains and their solutions.

Published
2021
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14. The KMT2Arecombinome of acute leukemias in 2023

Author

Meyer, C., Larghero, P., Almeida Lopes, B., Burmeister, T., Gröger, D., Sutton, R., Venn, N. C., Cazzaniga, G., Corral Abascal, L., Tsaur, G., Fechina, L., Emerenciano, M., Pombo-de-Oliveira, M. S., Lund-Aho, T., Lundán, T., Montonen, M., Juvonen, V., Zuna, J., Trka, J., Ballerini, P., Lapillonne, H., Van der Velden, V. H. J., Sonneveld, E., Delabesse, E., de Matos, R. R. C., Silva, M. L. M., Bomken, S., Katsibardi, K., Keernik, M., Grardel, N., Mason, J., Price, R., Kim, J., Eckert, C., Lo Nigro, L., Bueno, C., Menendez, P., zur Stadt, U., Gameiro, P., Sedék, L., Szczepański, T., Bidet, A., Marcu, V., Shichrur, K., Izraeli, S., Madsen, H. O., Schäfer, B. W., Kubetzko, S., Kim, R., Clappier, E., Trautmann, H., Brüggemann, M., Archer, P., Hancock, J., Alten, J., Möricke, A., Stanulla, M., Lentes, J., Bergmann, A. K., Strehl, S., Köhrer, S., Nebral, K., Dworzak, M. N., Haas, O. A., Arfeuille, C., Caye-Eude, A., Cavé, H., and Marschalek, R.

Abstract

Chromosomal rearrangements of the human KMT2A/MLLgene are associated with de novoas well as therapy-induced infant, pediatric, and adult acute leukemias. Here, we present the data obtained from 3401 acute leukemia patients that have been analyzed between 2003 and 2022. Genomic breakpoints within the KMT2Agene and the involved translocation partner genes (TPGs) and KMT2A-partial tandem duplications (PTDs) were determined. Including the published data from the literature, a total of 107 in-frame KMT2Agene fusions have been identified so far. Further 16 rearrangements were out-of-frame fusions, 18 patients had no partner gene fused to 5’-KMT2A, two patients had a 5’-KMT2Adeletion, and one ETV6::RUNX1patient had an KMT2Ainsertion at the breakpoint. The seven most frequent TPGs and PTDs account for more than 90% of all recombinations of the KMT2A, 37 occur recurrently and 63 were identified so far only once. This study provides a comprehensive analysis of the KMT2Arecombinome in acute leukemia patients. Besides the scientific gain of information, genomic breakpoint sequences of these patients were used to monitor minimal residual disease (MRD). Thus, this work may be directly translated from the bench to the bedside of patients and meet the clinical needs to improve patient survival.

Published
2023
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16. Sequencing of pediatric Burkitt lymphoma within the ICGC MMML-Seq project: 12

Author

Richter, J, Schlesner, M, Leich, E, Burkhardt, B, Hoffmann, S, Szczepanowski, M, Kreuz, M, Lenze, D, Bernhart, S, Rosolowski, M, Hoell, J, Lawerenz, C, Jâger, N, Hutter, B, Langerberger, D, Ammerpohl, O, Binder, H, Borkhardt, A, Brors, B, Claviez, A, Pischimarov, J, Dreyling, M, Eils, R, Hansmann, M-L, Binder, V, Hornig, N, Klapper, W, Korbel, J, Kube, D, Kûppers, R, Lichter, P, Loeffler, M, Môller, P, Pott, C, Rosenwald, A, Schreiber, Sefan, Schilhabel, M, Scholtysik, R, Stadler, P F, Trautmann, H, Wagener, R, Zenz, T, Trûmper, L, Rosenstiel, P, Hummel, M, and Siebert, R

Published
2012

21. Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time

Author

Yang, K., Emmerich, M.T.M., Deutz, A.H., Fonseca, C.M., Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y., Grimme, C., Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y., and Grimme, C.

Subjects
Global Optimization, Multiobjective Optimization, Time Complexity, Computational Geometry, Asymptotically Optimal Algorithm, Computational Integration
Published
2017

22. S129 TP53 SOMATIC MUTATIONS AS PRE-LEUKEMIC EVENTS IN ACUTE LYMPHOBLASTIC LEUKEMIA

Author

Chitadze, G., primary, Stengel, A., additional, John-Klaua, C., additional, Trautmann, H., additional, knecht, H., additional, Schwarz, M., additional, Kotrova, M., additional, Hollmann, J., additional, Hermann, D., additional, Appelt, F., additional, Theocharis, T., additional, Pal, K., additional, Darzentas, N., additional, Oberg, H.-H., additional, Kabelitz, D., additional, Gökbuget, N., additional, Baldus, C., additional, Haferlach, C., additional, and Brüggemann, M., additional

Published
2019
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23. Building and using an ontology of preference-based multiobjective evolutionary algorithms

Author

Li, Longmei, Yevseyeva, Iryna, Trautmann, H., Jing, N., and Emmerich, M. T. M.

Subjects
ComputingMethodologies_ARTIFICIALINTELLIGENCE
Abstract

Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.

Published
2017

24. Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412)

Author

Hoos, H.H., Neumann, F., and Trautmann, H.

Subjects
000 Computer science, knowledge, general works, machine learning, optimisation, AI, Computer Science, algorithm configuration, features, algorithm selection, performance prediction, empirical algorithmics
Abstract

This report documents the programme and the outcomes of Dagstuhl Seminar 16412 "Automated Algorithm Selection and Configuration", which was held October 9--14, 2016 and attended by 34 experts from 10 countries. Research on automated algorithm selection and configuration has lead to some of the most impressive successes within the broader area of empirical algorithmics, and has proven to be highly relevant to industrial applications. Specifically, high-performance algorithms for cnp-hard problems, such as propositional satisfiability (SAT) and mixed integer programming (MIP), are known to have a huge impact on sectors such as manufacturing, logistics, healthcare, finance, agriculture and energy systems, and algorithm selection and configuration techniques have been demonstrated to achieve substantial improvements in the performance of solvers for these problems. Apart from creating synergy through close interaction between the world's leading groups in the area, the seminar pursued two major goals: to promote and develop deeper understanding of the behaviour of algorithm selection and configuration techniques and to lay the groundwork for further improving their efficacy. Towards these ends, the organisation team brought together a group of carefully chosen researchers with strong expertise in computer science, statistics, mathematics, economics and engineering; a particular emphasis was placed on bringing together theorists, empiricists and experts from various application areas, with the goal of closing the gap between theory and practice.

Published
2017

35. Work Roll Cooling System Design Optimisation in Presence of Uncertainty

Author

Azene, Y. T., Rajkumar Roy, Farrugia, D., Onisa, C., Mehnen, J., Trautmann, H., Rajkumar Roy, and Essam Shehab

Subjects
Roll cooling design, Design optimisation, Uncertainty, Multi-objective optimisation
Abstract

Organised by: Cranfield University The paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops Meta models from a set of Finite Element Analysis (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective algorithm is applied to the design optimisation I order to identify a set of good solutions in the presence of uncertainties both in the decision and objective spaces. Mori Seiki – The Machine Tool Company

Published
2009

39. Germline variants in IKZF1, ARID5B, and CEBPE as risk factors for adult-onset acute lymphoblastic leukemia: an analysis from the GMALL study group

Author

Burmeister, T., primary, Bartels, G., additional, Groger, D., additional, Trautmann, H., additional, Schwartz, S., additional, Lenz, K., additional, Tietze-Burger, C., additional, Viardot, A., additional, Wasch, R., additional, Horst, H.-A., additional, Reinhardt, R., additional, Gokbuget, N., additional, Hoelzer, D., additional, Kneba, M., additional, and Bruggemann, M., additional

Published
2014
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40. Next-generation sequencing and real-time quantitative PCR for minimal residual disease detection in B-cell disorders

Author

Ladetto, M, primary, Brüggemann, M, additional, Monitillo, L, additional, Ferrero, S, additional, Pepin, F, additional, Drandi, D, additional, Barbero, D, additional, Palumbo, A, additional, Passera, R, additional, Boccadoro, M, additional, Ritgen, M, additional, Gökbuget, N, additional, Zheng, J, additional, Carlton, V, additional, Trautmann, H, additional, Faham, M, additional, and Pott, C, additional

Published
2013
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