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Number of items: 4.

Tarca, A. L. and Lauria, M. and Unger, M. and Bilal, E. and Boue, S. and Kumar Dey, K. and Hoeng, J. and Koeppl, H. and Martin, F. and Meyer, P. and Nandy, P. and Norel, R. and Peitsch, M. and Rice, J. and Romero, R. and Stolovitzky, G. and Talikka, M. and Xiang, Y. and Zechner, C. (2013):
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.
In: Bioinformatics (Oxford, England), pp. 2892-2899, 29, (22), [Online-Edition: http://www.ncbi.nlm.nih.gov/pubmed/23966112],
[Article]

Nandy, P. and Unger, M. and Zechner, C. and Dey, K. and Koeppl, H. (2013):
Learning diagnostic signatures from microarray data using Ll-regularized logistic regression.
In: Systems Biomedicine, Taylor & Francis, 1, (4), [Online-Edition: http://www.tandfonline.com/doi/full/10.4161/sysb.25271?mobil...],
[Article]

Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H. (2012):
Optimal variational perturbations for the inference of stochastic reaction dynamics.
IEEE, In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Nandy, P. and Unger, M. and Zechner, C. and Koeppl, H. (2012):
Optimal Perturbations for the Identification of Stochastic Reaction Dynamics.
Elsevier, In: 16th IFAC Symposium on System Identification, [Online-Edition: http://www.ifac-papersonline.net/Detailed/54661.html],
[Conference or Workshop Item]

This list was generated on Tue Nov 12 00:21:24 2019 CET.