The following list only includes journal articles and contributions to edited books and peer-reviewed
conferences since 2006. For a complete list of publications, see my
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[2]
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V. Steinwandter, M. Šišmiš, P. Sagmeister, U. Bodenhofer, and
C. Herwig.
Multivariate analytics of chromatographic data: Visual computing
based on moving window factor models.
J. Chromatogr. B., 1092:179--190, 2018.
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[1]
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S. Fischer, C. M. Freuling, T. Müller, F. Pfaff, U. Bodenhofer, D. Höper,
M. Fischer, D. A. Marston, A. R. Fooks, T. C. Mettenleitner, F. J. Conraths,
and T. Homeier-Bachmann.
Defining objective clusters for rabies virus sequences using affinity
propagation clustering.
PLoS Neglect. Trop. Dis., 12(1):e0006182, 2018.
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DOI ]
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[4]
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V. Greiff, C. R. Weber, J. Palme, U. Bodenhofer, E. Miho, U. Menzel, and S. T.
Reddy.
Learning the high-dimensional immunogenomic features that predict
public and private antibody repertoires.
J. Immunol., 199(8):2985--2997, 2017.
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DOI ]
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[3]
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E. P. Klement, P. Bauer, U. Bodenhofer, M. Mittendorfer-Holzer, R. Pollak,
R. Richter, and H. Exner.
PapaGeno --- vollautomatische menschenähnliche
Qualitätskontrolle für Aufdrucke auf Compact Discs.
In M. Wirth, A. Reichl, and M. Gräser, editors, 50 Jahre
Johannes Kepler Universität Linz. Innovationsfelder in Forschung, Lehre und
universitärem Alltag, pages 265--282. Böhlau, Vienna, Austria, 2017.
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[2]
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U. Bodenhofer, B. Haslinger-Eisterer, A. Minichmayer, G. Hermanutz, and
J. Meier.
Machine learning-based risk profile classification: A case study for
heart valve surgery.
In NIPS Workshop on Machine Learning for Health, Long Beach,
CA, December 2017.
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[1]
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U. Bodenhofer and S. Hochreiter.
Position kernels as a key to making sense of very rare and private
single-nucleotide variants.
In NIPS Workshop on Machine Learning in Computational Biology,
Long Beach, CA, December 2017.
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[4]
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A. Khaledi, M. Schniederjans, S. Pohl, R. Rainer, U. Bodenhofer, B. Xia,
F. Klawonn, S. Bruchmann, M. Preusse, D. Eckweiler, A. Dötsch, and
S. Häussler.
Transcriptome profiling of antimicrobial resistance in
Pseudomonas aeruginosa.
Antimicrob. Agents Chemother., 60(8):4722--4733, 2016.
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[3]
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N. Perualila-Tan, Z. Shkedy, W. Talloen, H. W. H. Göhlmann, The QSTAR Consortium, M. V. Moerbeke, and A. Kasim.
Weighted similarity-based clustering of chemical structures and
bioactivity data in early drug discovery.
J. Bioinform. Comput. Biol., 14:1650018, 2016.
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[2]
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N. Perualila-Tan, A. Kasim, W. Talloen, B. Verbist, H. W. H. Göhlmann, The QSTAR Consortium, and Z. Shkedy.
A joint modeling approach for uncovering associations between gene
expression, bioactivity and chemical structure in early drug discovery to
guide lead selection and genomic biomarker development.
Stat. Appl. Genet. Mol. Biol., 15(4):291--304, 2016.
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[1]
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F. Zehentmayr, C. Hauser-Kronberger, B. Zellinger, F. Hlubek, C. Schuster,
U. Bodenhofer, G. Fastner, H. Deutschmann, P. Steininger, R. Reitsamer,
T. Fischer, and F. Sedlmayer.
Hsa-miR-375 is a predictor of local control in early stage breast
cancer.
Clin. Epigenetics, 8:28, 2016.
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[6]
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U. Bodenhofer, E. Bonatesta, C. Horejš-Kainrath, and S. Hochreiter.
msa: an R package for multiple sequence alignment.
Bioinformatics, 31(24):3997--3999, 2015.
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[5]
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B. M. P. Verbist, G. R. Verheyen, L. Vervoort, M. Crabbe, D. Beerens,
C. Bosmans, S. Jaensch, S. Osselaer, W. Talloen, I. Van den Wyngaert,
G. Van Hecke, D. Wuyts, The QSTAR Consortium, F. Van Goethem, and
H. W. H. Göhlmann.
Integrating high-dimensional transcriptomics and image analysis tools
into early safety screening: proof of concept for a new early drug
development strategy.
Chem. Res. Toxicol., 28(10):1914--1925, 2015.
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DOI ]
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[4]
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J. Palme, S. Hochreiter, and U. Bodenhofer.
KeBABS: an R package for kernel-based analysis of biological
sequences.
Bioinformatics, 31(15):2574--2576, 2015.
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DOI ]
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[3]
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B. Verbist, G. Klambauer, L. Vervoort, W. Talloen, The QSTAR Consortium,
Z. Shkedy, O. Thas, A. Bender, H. W. Göhlmann, and S. Hochreiter.
Using transcriptomics to guide lead optimization in drug discovery
projects: Lessons learned from the QSTAR project.
Drug Discov. Today, 20(5):505--513, 2015.
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[2]
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A. C. Ravindranath, N. Perualila-Tan, A. Kasim, G. Drakakis, S. Liggi, S. C.
Brewerton, D. Mason, M. J. Bodkin, D. A. Evans, A. Bhagwat, W. Talloen, H. W.
Göhlmann, Z. Shkedy, A. Bender, and The QSTAR Consortium.
Connecting gene expression data from connectivity map and in silico
target predictions for small molecule mechanism-of-action analysis.
Mol. Biosyst., 11(1):86--96, 2015.
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DOI ]
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[1]
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L. Běhounek, U. Bodenhofer, P. Cintula, S. Saminger-Platz, and P. Sarkoci.
Graded dominance and related graded properties of fuzzy connectives.
Fuzzy Sets and Systems, 262:78--101, 2015.
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[2]
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K. Schwarzbauer, U. Bodenhofer, and S. Hochreiter.
Genome-wide chromatin remodeling identified at GC-rich long
nucleosome-free regions.
PLoS ONE, 7(11):e47924, 2012.
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G. Klambauer, K. Schwarzbauer, A. Mayr, D.-A. Clevert, A. Mitterecker,
U. Bodenhofer, and S. Hochreiter.
cn.MOPS: mixture of Poissons for discovering copy number
variations in next-generation sequencing data with a low false discovery
rate.
Nucleic Acids Res., 40(9):e69, 2012.
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[3]
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L. Běhounek, P. Cintula, U. Bodenhofer, S. Saminger-Platz, and P. Sarkoci.
On a graded notion of t-norm and dominance.
In Proc. 40th IEEE Int. Symp. on Multiple-Valued Logic, pages
73--78. IEEE Computer Society, 2010.
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[2]
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S. Hochreiter, U. Bodenhofer, M. Heusel, A. Mayr, A. Mitterecker, A. Kasim,
T. Khamiakova, S. Van Sanden, D. Lin, W. Talloen, L. Bijnens, H. W. H.
Göhlmann, Z. Shkedy, and D.-A. Clevert.
FABIA: factor analysis for bicluster acquisition.
Bioinformatics, 26(12):1520--1527, 2010.
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M. Štěpnička, U. Bodenhofer, M. Daňková, and V. Novák.
Continuity issues of the implicational interpretation of fuzzy rules.
Fuzzy Sets and Systems, 161(14):1959--1972, 2010.
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[3]
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D. Soukup, U. Bodenhofer, M. Mittendorfer-Holzer, and K. Mayer.
Semi-automatic identification of print layers from a sequence of
sample images: a case study from banknote print inspection.
Image Vision Comput., 27(8):989--998, 2009.
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[2]
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U. Bodenhofer, K. Schwarzbauer, M. Ionescu, and S. Hochreiter.
Modeling position specificity in sequence kernels by fuzzy
equivalence relations.
In J. P. Carvalho, D. Dubois, U. Kaymak, and J. M. C. Sousa, editors,
Proc. Joint 13th IFSA World Congress and 6th EUSFLAT Conference, pages
1376--1381, Lisbon, July 2009.
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U. Bodenhofer.
A survey of applications of fuzzy orderings: from databases to
statistics and machine learning.
In Proc. 14 Recontres Francophones sur la Logique Floue et ses
Applications, pages 3--9, Annecy, November 2009.
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[6]
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U. Bodenhofer.
Orderings of fuzzy sets based on fuzzy orderings. part II:
generalizations.
Mathware Soft Comput., 15(3):219--249, 2008.
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[5]
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U. Bodenhofer.
Orderings of fuzzy sets based on fuzzy orderings. part I: the basic
approach.
Mathware Soft Comput., 15(2):201--218, 2008.
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[4]
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U. Bodenhofer and F. Klawonn.
Robust rank correlation coefficients on the basis of fuzzy orderings:
initial steps.
Mathware Soft Comput., 15(1):5--20, 2008.
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[3]
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U. Bodenhofer and M. Demirci.
Strict fuzzy orderings with a given context of similarity.
Internat. J. Uncertain. Fuzziness Knowledge-Based Systems,
16(2):147--178, 2008.
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[2]
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L. Běhounek, U. Bodenhofer, and P. Cintula.
Relations in Fuzzy Class Theory: Initial steps.
Fuzzy Sets and Systems, 159(14):1729--1772, 2008.
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U. Bodenhofer.
Lexicographic composition of similarity-based fuzzy orderings.
In B. Bouchon-Meunier, C. Marsala, M. Rifqi, and R. R. Yager,
editors, Uncertainty and Intelligent Information Systems, chapter 33,
pages 457--469. World-Scientific, 2008.
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[4]
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U. Bodenhofer, B. De Baets, and J. Fodor.
A compendium of fuzzy weak orders: Representations and constructions.
Fuzzy Sets and Systems, 158(8):811--829, 2007.
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[3]
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U. Bodenhofer, M. Daňková, M. Štěpnička, and V. Novák.
A plea for the usefulness of the deductive interpretation of fuzzy
rules in engineering applications.
In Proc. 16th IEEE Int. Conf. on Fuzzy Systems, pages
1567--1572, London, July 2007.
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[2]
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U. Bodenhofer and F. Klawonn.
Towards robust rank correlation measures for numerical observations
on the basis of fuzzy orderings.
In M. Štěpnička, V. Novák, and U. Bodenhofer,
editors, Proc. 5th Conference of the European Society for Fuzzy Logic
and Technology, volume I, pages 321--327, Ostrava, September 2007.
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L. Běhounek, U. Bodenhofer, and P. Cintula.
Valverde-style representation results in a graded framework.
In M. Štěpnička, V. Novák, and U. Bodenhofer,
editors, Proc. 5th Conference of the European Society for Fuzzy Logic
and Technology, volume I, pages 153--160, Ostrava, September 2007.
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[7]
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U. Bodenhofer, B. De Baets, and J. Fodor.
General representation theorems for fuzzy weak orders.
In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt,
editors, Theory and Applications of Relational Structures as Knowledge
Instruments II, volume 4342 of Lecture Notes in Artificial
Intelligence, pages 229--244. Springer, Berlin, 2006.
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[6]
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U. Bodenhofer, J. Küng, and S. Saminger.
Flexible query answering using distance-based fuzzy relations.
In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt,
editors, Theory and Applications of Relational Structures as Knowledge
Instruments II, volume 4342 of Lecture Notes in Artificial
Intelligence, pages 207--228. Springer, Berlin, 2006.
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[5]
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S. Saminger, U. Bodenhofer, E. P. Klement, and R. Mesiar.
Aggregration of fuzzy relations and preservation of transitivity.
In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt,
editors, Theory and Applications of Relational Structures as Knowledge
Instruments II, volume 4342 of Lecture Notes in Artificial
Intelligence, pages 185--206. Springer, Berlin, 2006.
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[4]
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B. Moser and U. Bodenhofer.
Correspondences between fuzzy equivalence relations and kernels:
theoretical results and potential applications.
In Proc. 15th IEEE Int. Conf. on Fuzzy Systems, pages
10217--10223, Vancouver, BC, July 2006.
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[3]
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R. Bergmair and U. Bodenhofer.
Syntax-driven analysis of context-free languages with respect to
fuzzy relational semantics.
In Proc. 15th IEEE Int. Conf. on Fuzzy Systems, pages
9647--9654, Vancouver, BC, July 2006.
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[2]
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U. Bodenhofer.
Lexicographic composition of fuzzy orderings.
In Proc. 11th Int. Conf. on Information Processing and
Management of Uncertainty in Knowledge-Based Systems, volume 3, pages
2430--2437, Paris, July 2006.
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E. Lughofer and U. Bodenhofer.
Incremental learning of fuzzy basis function networks with a modified
version of vector quantization.
In Proc. 11th Int. Conf. on Information Processing and
Management of Uncertainty in Knowledge-Based Systems, volume 1, pages
56--63, Paris, July 2006.
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