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Bibliography

References to publications, sorted by year and author.

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2010	Sonnenburg, S, Rätsch, G, Henschel, S, Widmer, C, Behr, J, Zien, A, de Bona, F, Binder, A, Gehl, C, and Franc, V (2010). The SHOGUN Machine Learning Toolbox Journal of Machine Learning Research, 11:1799-1802. Printable file

Spencer, WC, Zeller, G, Watson, JD, Henz, SR, Watkins, KL, McWhirter, RD, Petersen, S, Sreedharan, VT, Widmer, C, Reinke, V, Petrella, L, Strome, S, Von Stetina, S, Katz, M, Rätsch, G, and Miller III, DM (2010).
A  Spatial  and  Temporal  Map  of  C.  elegans  Gene  Expression
Genome Research.

Supplementary website

Stegle, O, Drewe, P, Bohnert, R, Borgwardt, K, and Rätsch, G (2010).
Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts
Preprint.

Online Access

Supplementary website

Toussaint, NC, Widmer, C, Kohlbacher, O, and Rätsch, G (2010).
Exploiting Physico-Chemical Properties in String Kernels
BMC Bioinformatics, 11(Suppl. 8):S7.

Online Access

Widmer, C, Leiva, J, Altun, Y, and Rätsch, G (2010).
Leveraging Sequence Classification by Taxonomy-based Multitask Learning
In: Proc. RECOMB'10.

Widmer, C, Toussaint, N, Altun, Y, and Rätsch, G (2010).
Inferring Latent Task Structure for Multi-Task Learning by Multiple Kernel Learning
BMC Bioinformatics, 11(Suppl. 8):S5.

Online Access

Widmer, C, Toussaint, N, Altun, Y, Kohlbacher, O, and Rätsch, G (2010).
Novel Machine Learning Methods for MHC Class I Binding Prediction
In: Proc. Pattern Recognition in Bioinformatics, Springer. Lecture Notes in Computer Science.

Zhao, Z, Andersen, S, Ljung, K, Dolezal, K, Miotk, A, Schultheiss, SJ, and Lohmann, JU (2010).
Hormonal control of the shoot stem cell niche
Nature, 465:1089–1092.

Online Access

2009	Bohnert, R, Behr, J, and Rätsch, G (2009). Transcript quantification with RNA-Seq data BMC Bioinformatics, 10(S13):P5. Online Access Printable file

Dietmann, S, Georgii, E, Antonov, A, Tsuda, K, and Mewes, H (2009).
The DICS repository: module-assisted analysis of disease-related gene lists
Bioinformatics, 25(6):830-831.

Franc, V and Sonnenburg, S (2009).
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
Journal of Machine Learning Research, 10:2157--2192.

Georgii, E, Dietmann, S, Uno, T, Pagel, P, and Tsuda, K (2009).
Enumeration of condition-dependent dense modules in protein interaction networks
Bioinformatics, 25(7):933-940.

Georgii, E, Tsuda, K, and Schölkopf, B (2009).
Multi-Way Set Enumeration in Real-Valued Tensors
In: Data Mining using Matrices and Tensors (DMMT'09), ed. by Chris Ding, Tao Li, pp. 32-41, ACM.

Jenssen, R, Kloft, M, Zien, A, Sonnenburg, S, and Müller, K (2009).
A Multi-Class Support Vector Machine Based on Scatter Criteria
Technical University Berlin, Technical Report(014-2009).

Online Access

Kloft, M, Brefeld, U, Sonnenburg, S, Laskow, P, Mueller, K, and Zien, A (2009).
Efficient and Accurate Lp-Norm MKL
In: Advances in Neural Information Processing Systems 21, ed. by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta, pp. 997--1005, Cambridge, MA, MIT Press.

McNally, KL, Childs, KL, Bohnert, R, Davidson, RM, Zhao, K, Ulat, VJ, Zeller, G, Clark, RM, Hoen, DR, Bureau, TE, Stokowski, R, Ballinger, DG, Frazer, KA, Cox, DR, Padhukasahasram, B, Bustamante, CD, Weigel, D, Mackill, DJ, Bruskiewich, RM, Rätsch, G, Buell, CR, Leung, H, and Leach, JE (2009).
Genomewide SNP variation reveals relationships among landraces and modern varieties of rice
Proceedings of the National Academy of Sciences, 106(30):12273-8.

Online Access

Supplementary website

Naouar, N, Vandepoele, K, Lammens, T, Casneuf, T, Zeller, G, van Hummelen, P, Weigel, D, Rätsch, G, Inzé, D, Kuiper, M, De Veylder, L, and Vuylsteke, M (2009).
Quantitative RNA expression analysis with Affymetrix Tiling 1.0R Arrays identifies new E2F target genes
Plant Journal, 57(1):184-194.

Plantegenet, S, Weber, J, Goldstein, DR, Zeller, G, Nussbaumer, C, Thomas, J, Weigel, D, Harshmann, K, and Hardtke, CS (2009).
Comprehensive analysis of Arabidopsis expression level polymorphisms with simple inheritance
Molecular Systems Biology, 5:Article 242.

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2009).
KIRMES: Kernel-based identification of regulatory modules in euchromatic sequences
Bioinformatics, 25(16):2126-2133.

Online Access

Schultheiss, SJ, Busch, W, Lohmann, JU, Kohlbacher, O, and Rätsch, G (2009).
KIRMES: kernel-based identification of regulatory modules in euchromatic sequences
BMC Bioinformatics, 10(Suppl 13):O1.

Online Access

Schweikert, G, Behr, J, Zien, A, Zeller, G, Ong, CS, Sonnenburg, S, and Rätsch, G (2009).
mGene.web: a web service for accurate computational gene finding
Nucleic Acids Research, 37(Web Server issue):W312–W316.

Online Access

Schweikert, G, Zien, A, Zeller, G, Behr, J, Dieterich, C, Ong, CS, Philips, P, De Bona, F, Hartmann, L, Bohlen, A, Krüger, N, Sonnenburg, S, and Rätsch, G (2009).
mGene: Accurate SVM-Based Gene Finding with an Application to Nematode Genomes
Genome Research, 19:2133-2143.

Online Access

Supplementary website

Zeller, G (2009).
Machine Learning Algorithms for the Analysis of Data from Whole-Genome Tiling Microarrays
PhD thesis, Universitaet Tuebingen.

Online Access

Zeller, G, Henz, SR, Widmer, CK, Sachsenberg, T, Rätsch, G, Weigel, D, and Laubinger, S (2009).
Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole genome tiling arrays
Plant Journal, 58(6):1068-1082.

Online Access

Zien, A, Krämer, N, Sonnenburg, S, and Rätsch, G (2009).
The Feature Importance Ranking Measure
In: Proc. ECML PKDD, ed. by W. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor, vol. 5782/2009, pp. 694-709, Springer Berlin / Heidelberg, Springer, XXIX ed.. Lecture Notes in Artificial Intelligence.

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