@inproceedings{procKL1999,
title = {Rapid Software Prototyping in Computational Molecular Biology},
author = {O Kohlbacher and H -P Lenhof},
year = {1999},
date = {1999-01-01},
booktitle = {Proceedings of the German Conference on Bioinformatics (GCB'99)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{procNHL1998,
title = {Investigating the Sugar-Lectin Interaction by Computational Chemistry: Tunneling the Epithelial Barrier},
author = {Dirk Neumann and Elleonore Haltner and Claus-Michael Lehr and Oliver Kohlbacher and Hans-Peter Lenhof},
year = {1998},
date = {1998-01-01},
booktitle = {Abstracts of the 18th Interlec Meeting},
pages = {549},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@proceedings{MultiFactorial_DILS2018,
author = {Andreas Friedrich and Luis de la Garza and Oliver Kohlbacher},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Lara Schneider Tim Kehl, Kristina Thedinga Nadja Grammes Christina Backes Christopher Mohr Benjamin Schubert Kerstin Lenhof Nico Gerstner Andreas Daniel Hartkopf Markus Wallwiener Oliver Kohlbacher Andreas Keller Eckart Meese Norbert Graf Hans-Peter Lenhof L
ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification
@article{SchneiderClinOmics2019,
title = {ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification},
author = {Lara Schneider, Tim Kehl, Kristina Thedinga, Nadja L. Grammes, Christina Backes, Christopher Mohr, Benjamin Schubert, Kerstin Lenhof, Nico Gerstner, Andreas Daniel Hartkopf, Markus Wallwiener, Oliver Kohlbacher, Andreas Keller, Eckart Meese, Norbert Graf, Hans-Peter Lenhof},
journal = {Bioinformatics},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
@article{Richter2019,
title = {Food monitoring: Screening of the geographical origin of white asparagus using FT-NIR and machine learning},
author = {Bernadette Richter, Marc Rurik, Stephanie Gurk, Oliver Kohlbacher, Markus Fischer},
journal = {Food Control},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
@article{Choobdar265553b,
title = {Assessment of network module identification across complex diseases},
author = {Sarvenaz Choobdar and Mehmet E Ahsen and Jake Crawford and Mattia Tomasoni and Tao Fang and David Lamparter and Junyuan Lin and Benjamin Hescott and Xiaozhe Hu and Johnathan Mercer and Ted Natoli and Rajiv Narayan and and Aravind Subramanian and Jitao D Zhang and Gustavo Stolovitzky and Zoltán Kutalik and Kasper Lage and Donna K Slonim and Julio Saez-Rodriguez and Lenore J Cowen and Sven Bergmann and Daniel Marbach},
url = {https://www.nature.com/articles/s41592-019-0509-5.pdf},
doi = {10.1038/s41592-019-0509-5},
journal = {Nat. Methods},
volume = {16},
pages = {843–852},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the textquotedblleftDisease Module Identification DREAM Challengetextquotedblright, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the textquotedblleftDisease Module Identification DREAM Challengetextquotedblright, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).
@article{Wein_NASE_2020,
title = {A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry},
author = {Samuel Wein, Byron Andrews, Timo Sachsenberg, Helena Santos-Rosa, Oliver Kohlbacher, Tony Kouzarides, Benjamin Garcia, and Hendrik Weisser},
journal = {Nat. Commun.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
@article{FLASHDeconvCellSys2020,
title = {FLASHDeconv: Ultrafast, High-Quality Feature Deconvolution for Top-Down Proteomics},
author = {Kyowon Jeong and Jihyung Kim and Manasi Gaikwad and Siti Nurul Hidayah and Laura Heikaus and Hartmut Schlüter and Oliver Kohlbacher},
url = {https://doi.org/10.1016/j.cels.2020.01.003},
doi = {10.1016/j.cels.2020.01.003},
issn = {2405-4712},
journal = {Cell Systems},
publisher = {Elsevier},
abstract = {Top-down mass spectrometry (TD-MS)-based proteomics analyzes intact proteoforms and thus preserves information about individual protein species. The MS signal of these high-mass analytes is complex and challenges the accurate determination of proteoform masses. Fast and accurate feature deconvolution (i.e., the determination of intact proteoform masses) is, therefore, an essential step for TD data analysis. Here, we present FLASHDeconv, an algorithm achieving higher deconvolution quality, with an execution speed two orders of magnitude faster than existing approaches. FLASHDeconv transforms peak positions (m/z) within spectra into log m/z space. This simple transformation turns the deconvolution problem into a search for constant patterns, thereby greatly accelerating the process. In both simple and complex samples, FLASHDeconv reports more genuine feature masses and substantially fewer artifacts than other existing methods. FLASHDeconv is freely available for download here: https://www.openms.org/flashdeconv/. A record of this paper?s Transparent Peer Review process is included in the Supplemental Information.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
Top-down mass spectrometry (TD-MS)-based proteomics analyzes intact proteoforms and thus preserves information about individual protein species. The MS signal of these high-mass analytes is complex and challenges the accurate determination of proteoform masses. Fast and accurate feature deconvolution (i.e., the determination of intact proteoform masses) is, therefore, an essential step for TD data analysis. Here, we present FLASHDeconv, an algorithm achieving higher deconvolution quality, with an execution speed two orders of magnitude faster than existing approaches. FLASHDeconv transforms peak positions (m/z) within spectra into log m/z space. This simple transformation turns the deconvolution problem into a search for constant patterns, thereby greatly accelerating the process. In both simple and complex samples, FLASHDeconv reports more genuine feature masses and substantially fewer artifacts than other existing methods. FLASHDeconv is freely available for download here: https://www.openms.org/flashdeconv/. A record of this paper?s Transparent Peer Review process is included in the Supplemental Information.
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