Roland Nilsson (M.Sc, PhD)

nilsson@chgr.mgh.harvard.edu

I am currently a postdoc at Harvard while still being affiliated with the compmed team. I´ve been interested in a variety of topics concerning the application of engineering methods for problem solving in (molecular) biology and medicine. Recently my focus has been on statistical learning methods, ranging from hypothesis tests to support vector machines. I am also working with probe-level analysis of Affymetrix microarray data and developing software in Mathematica and Java for use in our lab.

Selected publications (from the compmed publication page)

  1. 61. Skogsberg et al .Transcriptional Profiling Uncovers a Network of
    Cholesterol-Responsive Atherosclerosis Target Genes     PLoS Genetics [To appear]

    58. Skogsberg,J., Dicker,A., Rydén,M., Åström, G., Nilsson, R., Mairal, A., Langin, D., Alberts, P., Walum, E., Tegnér, J., Hamsten, A., Arner, P., and J. Björkegren. Evidence that plasma low density lipoproteins containing apolipoprotein B100 regulate lipolysis in adipocytes.[To appear]

    54. Kovacs, A., Tornvall, P., Nilsson, R., Tegnér, J., Hamsten, A., and J. Björkegren, Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia. Proceedings of National Academy of Science Aug, 2007 [pdf] [Supp1] [Supp2]

    49. Nilsson, R., Peña, J. M., Björkegren J., and J. Tegnér, Detecting Multivariate Differentially Expressed Genes, BMC Bioinformatics 8:150  doi:10.1186/1471-2105-8-150, 2007 [pdf here] [Supp1] [Supp2] [Supp3] (Highly accessed)

    48. Nilsson, R., Peña, J. M., Björkegren J., and J. Tegnér, Consistent feature selection for pattern recognition in polynomial time, Journal of Machine Learning Research, 8(March): 589-612, 2007 [pdf here]

    47. Tegnér, J., Nilsson, R., Bajic, V.B., Björkegren, J. and T. Ravasi, Systems biology of innate immunity , Cellular Immunology, doi:10.1016/ j.cellimm.2007.01.010, 2006 [pdf here] (Invited review)

    • 40. Peña, J. M., Nilsson, R., Björkegren, J. and Tegnér, J. Towards scalable and Data Efficient Learning of Markov Boundaries, International Journal of Approximate Reasoning, 45(2), 211-232, 2007. [pdf here]

      38.Pena, J., Nilsson, R, Björkegren, J. and Tegnér, J. Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity, EWGP, 247-254, 2006 [pdf here]

      37.Nilsson, R., Pena, J., Björkegren, J. and Tegnér, J. Evaluating Feature Selection for SVMs in High Dimensions. Lecture Notes in Computer Science,719-726, Springer, 2006 [pdf here]

      36. Pena, J., Nilsson, R, Björkegren, J. and Tegnér, J. Identifying Relevant Nodes without Learning the Model , UAI, 367-374, 2006 [pdf here]

      34. Roland Nilsson, Vladimir B. Bajic, Shintaro Katayama, Harukazu Suzuki, Diego di Bernardo, Johan Björkegren, Matthew J. Sweet, Piero Carninci, Yosihide Hayashizaki, David A. Hume., Jesper Tegner, and Timothy Ravasi, Transcriptional Network Dynamics in Macrophage Activation, Genomics, Aug;88(2):133-142, 2006 [pdf here] [on-line supplement](2006 ScienceDirect TOP 10 DOWNLOADED ARTICLES)

      30. Nilsson R, Björkegren J, Tegnér J: A flexible implementation for support vector machines. The Mathematica Journal, Vol 10, 114-127, 2005 [pdf here]

    • 28. The Phantom3 Consortium, The transcriptional landscape of the mammalian genome, Science. 2005 Sep 2;309 (5740):1559-63. [pdf here] (Journal Cover and cited > 300)

    • 22. Nilsson R, Björkegren J, Tegnér: A powerful differential expression test for probe-level oligonucleotide microarray data. In proc. of 2nd IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 10-14, 2004 [pdf here]

 

 
 
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