Recent advances in molecular biotechnologies have fundamentally changed how cancer patients are diagnosed and treated. The development of targeted therapies has increased patients’ life expectancy and quality of life with the majority of cancer types. However, predicting treatment efficacy and selecting the optimal personalised therapy for each patient remains a challenge for clinicians. Mainly, the development of resistance to therapy and intratumoural heterogeneity limit successful long-term remissions and cures. The early prediction of therapy resistance or relapse is thus deemed crucial for further improving therapy outcome. Identification of features termed biomarkers, which are derived from patient samples by high-throughput analyses, is an important means to achieve this goal. Project C1 builds and optimises models for clinically relevant decisions in oncology by selecting features from high-dimensional feature spaces, extracted from raw data created on different molecular platforms.
In the past, highly parallel (“next generation”) DNA sequencing technology allowed researchers with access to specialised sequencing core facilities to discover tumour-specific mutations. As DNA sequencing capacity continues to increase and costs continue to drop faster than computational capacity and storage can keep up, new algorithmic paradigms are needed for the analysis of very large genomic data sets. In project C1, we investigate new algorithms to extract relevant features for biomarker discovery from whole-genome data sets in the 10–100 terabyte range on commodity hardware by streaming the sequence data and filtering for features of interest using novel string hashing methods.
Recent developments in nanopore sequencing are democratising DNA sequencing and genome analysis. The new nanopore sequencers are of size comparable to a USB stick, are inexpensive, and can be used without specialised lab equipment. While nanopore sequencing offers lower throughput and higher error rates than established technologies at the moment, it has the potential to turn DNA sequencing and subsequently genomic analysis into a commodity. In oncology, the vision is that nanopore sequencing, together with non-invasive patient monitoring techniques such as “liquid biopsies” drawn from blood or urine, will allow for detection of small amounts of circulating tumour DNAs, allowing an accurate assessment of patient risk and therapy options. In principle, such an assessment would be possible anywhere at any time, given standard equipment of moderate costs, i.e., the sequencer and a laptop or embedded system.
For this vision to become a reality, several data analysis challenges must be overcome: In addition to the constraints imposed by small sample size n compared to the high dimensionality p of the feature space (n << p problem), the cyber-physical systems for nanopore sequencing create novel resource constraints: The raw data generated by this new technology is a large-volume high-frequency signal of ion currents, which is difficult to translate directly into a DNA sequence. Therefore, to identify tumour “fingerprints” or biomarkers based on tracing tumour-derived nucleic acids, either better methods for DNA base calling from ion currents are needed, or a different representation of the tumour fingerprints has to be considered, such as features in signal space. We will follow both avenues in parallel and in particular consider novel features derived from a discretised compressed ion current signal space.
| Moelder/etal/2021a |
Mölder, Felix and Jablonski, Kim Philipp and Letcher, Brice and Hall, Michael B. and Tomkins-Tinch, Christopher H. and Sochat, Vanessa and Forster, Jan and Lee, Soohyun and Twardziok, Sven O. and Kanitz, Alexander and Wilm, Andreas and Holtgrewe, Manuel and Rahmann, Sven and Nahnsen, Sven and Köster, Johannes.
Sustainable data analysis with Snakemake.
In
F1000Research,
Vol. 10,
pages 33,
2021.
|
| Zentgraf/Rahmann/2021a |
Jens Zentgraf and Sven Rahmann.
Fast lightweight accurate xenograft sorting.
In
Algorithms Mol. Biol.,
Vol. 16,
No. 1,
pages 2,
2021.
|
| Kuthe/Rahmann/2020a |
Elias Kuthe and Sven Rahmann.
Engineering Fused Lasso Solvers on Trees.
In
Simone Faro and Domenico Cantone (editors),
18th International Symposium on Experimental Algorithms, SEA 2020, June 16-18, 2020, Catania, Italy,
Vol. 160,
pages 23:1--23:14,
Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
2020.
|
| Oeck/etal/2020a |
Oeck, Sebastian and Tüns, Alicia I. and Hurst, Sebastian and Schramm, Alexander.
Streamlining Quantitative Analysis of Long RNA Sequencing Reads.
In
International Journal of Molecular Sciences,
Vol. 21,
No. 19,
2020.
|
| Zentgraf/etal/2020a |
Jens Zentgraf and Henning Timm and Sven Rahmann.
Cost-optimal assignment of elements in genome-scale multi-way bucketed Cuckoo hash tables.
In
Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) 2020,
pages 186--198,
SIAM,
2020.
|
| Zentgraf/Rahmann/2020a |
Jens Zentgraf and Sven Rahmann.
Fast Lightweight Accurate Xenograft Sorting.
In
Carl Kingsford and Nadia Pisanti (editors),
20th International Workshop on Algorithms in Bioinformatics (WABI 2020),
Vol. 172,
pages 4:1--4:16,
Dagstuhl, Germany,
Schloss Dagstuhl--Leibniz-Zentrum für Informatik,
2020.
|
| Hess/etal/2019a |
Hess, Sibylle and Duivesteijn, Wouter and Honysz, Philipp-Jan and Morik, Katharina.
The SpectACl of Nonconvex Clustering: a Spectral Approach to Density-Based Clustering.
In
AAAI,
2019.
|
| Stoecker/etal/2019a |
Bianca K. St\"ocker and Till Sch\"afer and Petra Mutzel and Johannes K\"oster and Nils M. Kriege and Sven Rahmann.
Protein Complex Similarity Based on Weisfeiler-Lehman Labeling.
In
Giuseppe Amato and Claudio Gennaro and Vincent Oria and Milos Radovanovic (editors),
Similarity Search and Applications,
pages 308--322,
Cham,
Springer,
2019.
title = {Protein Complex Similarity Based on {W}eisfeiler-{L}ehman Labeling}, address = {Cham}, booktitle = {Similarity Search and Applications}, editor = {Giuseppe Amato and Claudio Gennaro and Vincent Oria and Milos Radovanovic}, year = {2019}, pages = {308--322}, publisher = {Springer International Publishing}, isbn = {978-3-030-32047-8}, abstract = {Proteins in living cells rarely act alone, but instead perform their functions together with other proteins in so-called protein complexes. Being able to quantify the similarity between two protein complexes is essential for numerous applications, e.g. for database searches of complexes that are similar to a given input complex. While the similarity problem has been extensively studied on single proteins and protein families, there is very little existing work on modeling and computing the similarity between protein complexes. Because protein complexes can be naturally modeled as graphs, in principle general graph similarity measures may be used, but these are often computationally hard to obtain and do not take typical properties of protein complexes into account. Here we propose a parametric family of similarity measures based on Weisfeiler-Lehman labeling. We evaluate it on simulated complexes of the extended human integrin adhesome network. We show that the defined family of similarity measures is in good agreement with edit similarity, a similarity measure derived from graph edit distance, but can be computed more efficiently. It can therefore be used in large-scale studies and serve as a basis for further refinements of modeling protein complex similarity.} }')">
|
| Ackermann/etal/2018a |
Ackermann, S. and Cartolano, M. and Hero, B. and Welte, A. and Kahlert, Y. and Roderwieser, A. and Bartenhagen, C. and Walter, E. and Gecht, J. and Kerschke, L. and Volland, R. and Menon, R. and Heuckmann, J. M. and Gartlgruber, M. and Hartlieb, S. and Henrich, K. O. and Okonechnikov, K. and Altmuller, J. and Nurnberg, P. and Lefever, S. and de Wilde, B. and Sand, F. and Ikram, F. and Rosswog, C. and Fischer, J. and Theissen, J. and Hertwig, F. and Singhi, A. D. and Simon, T. and Vogel, W. and Perner, S. and Krug, B. and Schmidt, M. and Rahmann, S. and Achter, V. and Lang, U. and Vokuhl, C. and Ortmann, M. and Buttner, R. and Eggert, A. and Speleman, F. and O'Sullivan, R. J. and Thomas, R. K. and Berthold, F. and Vandesompele, J. and Schramm, A. and Westermann, F. and Schulte, J. H. and Peifer, M. and Fischer, M..
A mechanistic classification of clinical phenotypes in neuroblastoma.
In
Science,
Vol. 362,
No. 6419,
pages 1165--1170,
2018.
|
| Hess/etal/2018a |
Hess, Sibylle and Piatkowski, Nico and Morik, Katharina.
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization.
In
Proceedings of the 2018 SIAM International Conference on Data Mining, SDM 2018, May 3-5, 2018, San Diego Marriott Mission Valley, San Diego, CA, USA.,
pages 405--413,
SIAM,
2018.
|
| Schulte/etal/2018a |
Schulte, M. and Köster, J. and Rahmann, S. and Schramm, A..
Cancer evolution, mutations, and clonal selection in relapse neuroblastoma.
In
Cell Tissue Research,
Vol. 372,
No. 2,
pages 263--268,
2018.
|
| Stoecker/etal/2018a |
Stöcker, Bianca K. and Schäfer, Till and Mutzel, Petra and Köster, Johannes and Kriege, Nils and Rahmann, Sven.
Protein Complex Similarity Based on Weisfeiler-Lehman Labeling.
In
PeerJ Preprints,
Vol. 6,
No. e26612,
2018.
|
| Hess/etal/2017a |
Hess, Sibylle and Morik, Katharina and Piatkowski, Nico.
The PRIMPING routine---Tiling through proximal alternating linearized minimization.
In
Data Mining and Knowledge Discovery,
Vol. 31,
No. 4,
pages 1090--1131,
2017.
|
| Hess/Morik/2017a |
Hess, Sibylle and Morik, Katharina.
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization.
In
Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017,
Springer,
2017.
|
| Horsch/etal/2017b |
Horsch, Salome and Kopczynski, Dominik and Kuthe, Elias and Baumbach, Jörg Ingo and Rahmann, Sven and Rahnenführer, Jörg.
A detailed comparison of analysis processes for MCC-IMS data in disease classification---Automated methods can replace manual peak annotations.
In
PLOS ONE,
Vol. 12,
No. 9,
pages e0184321,
2017.
|
| Quedenfeld/Rahmann/2017a |
Jens Quedenfeld and Sven Rahmann.
Analysis of Min-Hashing for Variant Tolerant DNA Read Mapping.
In
Russell Schwartz and Knut Reinert (editors),
17th International Workshop on Algorithms in Bioinformatics, WABI 2017, August 21-23, 2017, Boston, MA, USA,
Vol. 88,
pages 21:1--21:13,
Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik,
2017.
|
| Schroeder/Rahmann/2017a |
Schröder, Christopher and Rahmann, Sven.
A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification.
In
Algorithms for Molecular Biology,
Vol. 12,
pages 21,
2017.
|
| Shpacovitch/etal/2017a |
Shpacovitch, Victoria and Sidorenko, Irina and Lenssen, Jan Eric and Temchura, Vladimir and Weichert, Frank and Müller, Heinrich and Überla, Klaus and Zybin, Alexander and Schramm, Alexander and Hergenröder, Roland.
Application of the PAMONO-sensor for Quantification of Microvesicles and Determination of Nano-particle Size Distribution.
In
Sensors,
Vol. 17,
No. 2,
pages 1-14,
2017.
|
| Althoff/Schulte/2016a |
Althoff, Kristina and Schulte, Johannes and Schramm, Alexander.
Towards diagnostic application of non-coding RNAs in neuroblastoma.
In
Expert Review of Molecular Diagnostics,
Vol. 16,
No. 12,
pages 1307-1313,
2016.
|
| Consortium/2016a |
The Computational Pan-Genomics Consortium.
Computational pan-genomics: status, promises and challenges.
In
Briefings in Bioinformatics,
2016.
|
| Johansson/etal/2016a |
Johansson, Patricia and Bergmann, Anke and Rahmann, Sven and Wohlers, Inken and Scholtysik, René and Przekopowitz, Martina and Seifert, Marc and Tschurtschenthaler, Gertraud and Webersinke, Gerald and Jäger, Ulrich and Siebert, Reiner and Klein-Hitpass, Ludger and Dührsen, Ulrich and Dürig, Jan and Küppers, Ralf.
Recurrent alterations of TNFAIP3 (A20) in T-cell large granular lymphocytic leukemia.
In
International Journal of Cancer,
Vol. 138,
No. 1,
pages 121--124,
2016.
|
| Kliewer/Lee/2016a |
Kliewer, Viktoria and Lee, Sangkyun.
EasyTCGA: An R package for easy batch downloading of TCGA data from FireBrowse.
No. 4,
TU Dortmund,
2016.
|
| Lee/etal/2016a |
Lee, Sangkyun and Brzyski, Damian and Bogdan, Malgorzata.
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered l1-Norm.
In
Arthur Gretton and Christian C. Robert (editors),
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS),
pages 780--789,
JMLR W&CP,
2016.
|
| Lee/Holzinger/2016a |
Sangkyun Lee and Andreas Holzinger.
Knowledge Discovery from Complex High Dimensional Data.
In
Stefan Michaelis and Nico Piatkowski and Marco Stolpe (editors),
Solving Large Scale Learning Tasks. Challenges and Algorithms - Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday,
Vol. 9580,
pages 148--167,
Springer,
2016.
|
| Piatkowski/etal/2016a |
Piatkowski, Nico and Lee, Sangkyun and Morik, Katharina.
Integer undirected graphical models for resource-constrained systems.
In
Neurocomputing,
Vol. 173,
No. 1,
pages 9--23,
Elsevier,
2016.
|
| Riehl/Schulte/2016a |
Riehl, Lara and Schulte, Johannes and Mulaw, Medhanie and Dahlhaus, Maike and Fischer, Matthias and Schramm, Alexander and Eggert, Angelika and Debatin, Klaus-Michael and Beltinger, Christian.
The mitochondrial genetic landscape in neuroblastoma from tumor initiation to relapse.
In
Oncotarget,
Vol. 7,
pages 6620-6625,
2016.
|
| Schramm/Lode/2016a |
Schramm, Alexander and Lode, Holger.
MYCN-targeting vaccines and immunotherapeutics.
In
Human Vaccines & Immunotherapeutics,
Vol. 12,
No. 9,
pages 2257-2258,
2016.
|
| Schroeder/Rahmann/2016a |
Christopher Schröder and Sven Rahmann.
A Hybrid Parameter Estimation Algorithm for Beta Mixtures and Applications to Methylation State Classification.
In
Martin C. Frith and Christian Nørgaard Storm Pedersen (editors),
Algorithms in Bioinformatics - 16th International Workshop, WABI 2016, Aarhus, Denmark, August 22--24, 2016. Proceedings,
Vol. 9838,
pages 307--319,
Springer,
2016.
|
| Stoecker/etal/2016a |
Stöcker, B. K. and Köster, J. and Rahmann, S..
SimLoRD: Simulation of Long Read Data.
In
Bioinformatics,
Vol. 32,
No. 17,
pages 2704--2706,
2016.
|
| Berulava/etal/2015a |
Berulava, Tea and Rahmann, Sven and Rademacher, Katrin and Klein-Hitpass, Ludger and Horsthemke, Bernhard.
N6-Adenosine Methylation in miRNAs.
In
PLoS One,
Vol. 10,
No. 2,
pages e0118438,
2015.
|
| Hesse/etal/2015a |
Nina Hesse and Christopher Schröder and Sven Rahmann.
An optimization approach to detect differentially methylated regions from Whole Genome Bisulfite Sequencing data.
In
PeerJ PrePrints,
Vol. 3,
pages e1287,
2015.
|
| Lee/2015a |
Sangkyun Lee.
Signature Selection for Grouped Features with A Case Study on Exon Microarrays.
In
Urszula Stańczyk and Lakhmi C. Jain (editors),
Feature Selection for Data and Pattern Classification,
pages 329--349,
Springer,
2015.
|
| Lee/etal/2015b |
Lee, Sangkyun and Brzyski, Damian and Bogdan, Malgorzata.
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered $\ell_1$-Norm.
In
19th International Conference on Artificial Intelligence and Statistics,
2015.
|
| Schramm/etal/2015a |
Schramm, Alexander and Köster, Johannes and Assenov, Yassen and Althoff, Kristina and Peifer, Martin and Mahlow, Ellen and Odersky, Andrea and Beisser, Daniela and Ernst, Corinna and Henssen, Anton G. and Stephan, Harald and Schröder, Christopher and Heukamp, Lukas and Engesser, Anne and Kahlert, Yvonne and Theissen, Jessica and Hero, Barbara and Roels, Frederik and Altmüller, Janine and Nürnberg, Peter and Astrahantseff, Kathy and Gloeckner, Christian and De Preter, Katleen and Plass, Christoph and Lee, Sangkyun and Lode, Holger N. and Henrich, Kai-Oliver and Gartlgruber, Moritz and Speleman, Frank and Schmezer, Peter and Westermann, Frank and Rahmann, Sven and Fischer, Matthias and Eggert, Angelika and Schulte, Johannes H..
Mutational dynamics between primary and relapse neuroblastomas.
In
Nature Genetics,
Vol. 47,
No. 8,
pages 872--877,
2015.
|
| Schroeder/Rahmann/2015a |
Christopher Schröder and Sven Rahmann.
Efficient duplicate rate estimation from subsamples of sequencing libraries.
In
PeerJ PrePrints,
Vol. 3,
pages e1298,
2015.
|
| Schwermer/Lee/2015a |
Schwermer, Melanie and Lee, Sangkyun and Köster, Johannes and van Maerken, Tom and Stephan, Harald and Eggert, Angelika and Morik, Katharina and Schulte, Johannes H. and Schramm, Alexander.
Sensitivity to cdk1-inhibition is modulated by p53 status in preclinical models of embryonal tumors.
In
Oncotarget,
2015.
|
| Artikis/etal/2014a |
Alexander Artikis and Matthias Weidlich and Francois Schnitzler and Ioannis Boutsis and Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik and Vana Kalogeraki and Jakub Marecek and Avigdor Gal and Shie Mannor and Dimitrios Gunopulos and Dermot Kinane.
Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management.
In
Proceedings of the 17th International Conference on Extending Database Technology,
2014.
|
| Koester/Rahmann/2014a |
Johannes Köster and Sven Rahmann.
Massively parallel read mapping on GPUs with the q-group index and PEANUT.
In
PeerJ,
Vol. 2,
pages e606,
2014.
|
| Lee/2014a |
Lee, Sangkyun.
Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure.
In
Holzinger, Andreas and Jurisica, Igor (editors),
Interactive Knowledge Discovery and Data Mining in Biomedical Informatics,
Vol. 8401,
pages 227--240,
Springer,
2014.
|
| Lee/2014b |
Lee, Sangkyun.
Characterization of Subgroup Patterns from Graphical Representation of Genomic Data.
In
\'Sl\c ezak, Dominik and Tan, Ah-Hwee and Peters, JamesF. and Schwabe, Lars (editors),
Brain Informatics and Health,
Vol. 8609,
pages 516--527,
Springer,
2014.
|
| Lee/etal/2014a |
Sangkyun Lee and Jörg Rahnenführer and Michel Lang and Katleen de Preter and Pieter Mestdagh and Jan Koster and Rogier Versteeg and Raymond Stallings and Luigi Varesio and Shahab Asgharzadeh and Johannes Schulte and Kathrin Fielitz and Melanie Heilmann and Katharina Morik and Alexander Schramm.
Robust Selection of Cancer Survival Signatures from High-Throughput Genomic Data Using Two-Fold Subsampling.
In
PLoS ONE,
Vol. 9,
pages e108818,
2014.
|
| Lee/Poelitz/2014a |
Lee, Sangkyun and Pölitz, Christian.
Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-Limited Sensor Networks.
In
International Conference on Pattern Recognition Applications and Methods,
2014.
|
| Liebig/etal/2014d |
Thomas Liebig and Nico Piatkowski and Christian Bockermann and Katharina Morik.
Route Planning with Real-Time Traffic Predictions.
In
Proceedings of the LWA 2014 Workshops: KDML, IR, FGWM,
pages 83-94,
2014.
|
| Piatkowski/etal/2014a |
Piatkowski, Nico and Sangkyun, Lee and Morik,Katharina.
The Integer Approximation of Undirected Graphical Models.
In
De Marsico, Maria and Tabbone, Antoine and Fred, Ana (editors),
ICPRAM 2014 - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, ESEO, Angers, Loire Valley, France, 6-8 March, 2014,
pages 296--304,
SciTePress,
2014.
|
| Schnitzler/etal/2014b |
Schnitzler, Francois and Artikis, Alexander and Weidlich, Matthias and Boutsis, Ioannis and Liebig, Thomas and Piatkowski, Nico and Bockermann, Christian and Morik, Katharina and Kalogeraki, Vana and Marecek, Jakub and Gal, Avigdor and Mannor, Shie and Kinane, Dermot and Gunopulos, Dimitrios.
Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights.
In
Proceedings of the European Conference on Machine Learning (ECML), Nectar Track,
pages 520-523,
Springer,
2014.
|
| Lee/Schramm/2013a |
Lee, Sangkyun and Schramm, Alexander.
Preprocessing of Affymetrix Exon Expression Arrays.
No. 3,
Technische Universität Dortmund,
2013.
|
| Lee/Wright/2013a |
Lee, Sangkyun and Wright, Stephen J..
Stochastic Subgradient Estimation Training for Support Vector Machines.
In
Latorre Carmona, Pedro and S\'anchez, J. Salvador and Fred, Ana L.N. (editors),
Mathematical Methodologies in Pattern Recognition and Machine Learning,
Vol. 30,
pages 67--82,
Springer,
2013.
|
| Rahmann/etal/2013a |
Sven Rahmann and Marcel Martin and Johannes H. Schulte and Johannes Köster and Tobias Marschall and Alexander Schramm.
Identifying Transcriptional miRNA Biomarkers by Integrating High-Throughput Sequencing and Real-Time PCR Data.
In
Methods,
Vol. 59,
No. 1,
pages 154--163,
2013.
|
| Schramm/etal/2012a |
Alexander Schramm and Johannes Köster and Tobias Marschall and Marcel Martin and Melanie Heilmann and Kathrin Fielitz and Gabriele Büchel and Matthias Barann and Daniela Esser and Philip Rosenstiel and Sven Rahmann and Angelika Eggert and Johannes H. Schulte.
Next-generation RNA sequencing reveals differential expression of MYCN target genes and suggests the mTOR pathway as a promising therapy target in MYCN-amplified neuroblastoma.
In
International Journal of Cancer,
Vol. 132,
No. 3,
pages 154--163,
2013.
|
| Schulte/etal/2013a |
Schulte, J H and Lindner, S and Bohrer, A and Maurer, J and De Preter, K and Lefever, S and Heukamp, L and Schulte, S and Molenaar, J and Versteeg, R and Thor, T and Künkele, A and Vandesompele, J and Speleman, F and Schorle, H and Eggert, A and Schramm, A.
MYCN and ALKF1174L are sufficient to drive neuroblastoma development from neural crest progenitor cells.
In
Oncogene,
Vol. 32,
No. 8,
pages 1059--1065,
2013.
|
| Lee/2012a |
Lee, Sangkyun.
Improving Confidence of Dual Averaging Stochastic Online Learning via Aggregation.
In
German Conference on Artificial Intelligence (KI 2012),
pages 229--232,
2012.
|
| Lee/etal/2012a |
Lee, S. and Stolpe, M. and Morik, K..
Separable Approximate Optimization of Support Vector Machines for Distributed Sensing.
In
Flach, Peter A. and De Bie, Tijland and Cristianini, Nello (editors),
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II,
Vol. 7524,
pages 387--402,
Springer,
2012.
|
| Lee/Wright/2012a |
Lee, Sangkyun and Wright, Stephen J..
ASSET: Approximate Stochastic Subgradient Estimation Training for Support Vector Machines.
In
International Conference on Pattern Recognition Applications and Methods (ICPRAM 2012),
pages 223-228,
2012.
|
| Lee/Wright/2012b |
Lee, Sangkyun and Wright, Stephen J..
Manifold Identification in Dual Averaging Methods for Regularized Stochastic Online Learning.
In
Journal of Machine Learning Research,
Vol. 13,
pages 1705--1744,
2012.
|
| Molenaar/etal/2012a |
Molenaar, Jan J and Domingo-Fernandez, Raquel and Ebus, Marli E and Lindner, Sven and Koster, Jan and Drabek, Ksenija and Mestdagh, Pieter and van Sluis, Peter and Valentijn, Linda J and van Nes, Johan and Broekmans, Marloes and Haneveld, Franciska and Volckmann, Richard and Bray, Isabella and Heukamp, Lukas and Sprussel, Annika and Thor, Theresa and Kieckbusch, Kristina and Klein-Hitpass, Ludger and Fischer, Matthias and Vandesompele, Jo and Schramm, Alexander and van Noesel, Max M and Varesio, Luigi and Speleman, Frank and Eggert, Angelika and Stallings, Raymond L and Caron, Huib N and Versteeg, Rogier and Schulte, Johannes H.
LIN28B induces neuroblastoma and enhances MYCN levels via let-7 suppression.
In
Nature Genetics,
Vol. 44,
No. 11,
pages 1199--1206,
2012.
|
| Piatkowski/etal/2012a |
Piatkowski, Nico and Lee, Sangkyun and Morik, Katharina.
Spatio-Temporal Models For Sustainability.
In
Marwah, Manish and Ramakrishnan, Naren and Berges, Mario and Kolter, Zico (editors),
Proceedings of the SustKDD Workshop within ACM KDD 2012,
ACM,
2012.
|
| Schramm/etal/2012b |
Alexander Schramm and Benjamin Schowe and Kathrin Fielitz and Melanie Heilmann and Marcel Martin and Tobias Marschall and Johannes Köster and Jo Vandesompele and Joelle Vermeulen and Katleen de Preter, Jan Koster and Rogier Versteeg and Rosa Noguera and Frank Speleman and Sven Rahmann and Angelika Eggert and Katharina Morik and and Johannes H. Schulte.
Exon-level expression analyses identify MYCN and NTRK1 as major determinants of alternative exon usage and robustly predict primary neuroblastoma outcome.
In
British Journal of Cancer,
Vol. 107,
No. 8,
pages 1409--1417,
2012.
|
| Umaashankar/Lee/2012a |
Umaashankar, Venkatesh and Lee, Sangkyun.
Optimization plugin for RapidMiner.
No. 4,
TU Dortmund University,
2012.
|
| Esser/etal/2011a |
Esser, R. and Glienke, W. and Bochennek, K. and Erben, S. and Quaiser, A. and Pieper, C. and Eggert, A. and Schramm, A. and Astrahantseff, K. and Hansmann, M. L. and Schwabe, D. and Klingebiel, T. and Koehl U..
Detection of Neuroblastoma Cells during Clinical Follow Up: Advanced Flow Cytometry and RT-PCR for Tyrosine Hydroxylase Using Both Conventional and Real-Time PCR.
In
Klin Padiatr,
Vol. 223,
pages 326-331,
2011.
|
| Lee/Bockermann/2011a |
Lee, Sangkyun and Bockermann, Christian.
Scalable stochastic gradient descent with improved confidence.
In
Big Learning -- Algorithms, Systems, and Tools for Learning at Scale,
2011.
|
| Lee/etal/2011a |
Lee, Sangkyun and Schowe, Benjamin and Sivakumar, Viswanath and Morik, Katharina.
Feature Selection for High-Dimensional Data with RapidMiner.
No. 1,
TU Dortmund University,
2011.
|
| Schowe/Morik/2011b |
Schowe, Benjamin and Morik, Katharina.
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