Papers

  • Yaroslav Lyutvinskiy, et al. A Web Service Framework for Interactive Analysis of Metabolomics Data. Analytical Chemistry,  89:5713–5718, 2017.
  • Roland Nilsson, et al. Estimation of flux ratios without uptake or release data: application to serine and methionine metabolism. Metabolic Engineering (in press), 2017.
  • Irena Roci, et al. A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing. Journal of Visualized Experiments 120:e55011, 2017.
  • Roland Nilsson and Mohit Jain. Simultaneous tracing of carbon and nitrogen isotopes in human cells. Molecular Biosystems 12:1929-1937, 2016.
  • Irena Roci, et al. Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian CellsAnalytical Chemistry, 88:2707–2713,  2016.
  • Nina Gustafsson Sheppard, et al. The folate-coupled enzyme MTHFD2 is a nuclear protein and promotes cell proliferation. Scientific Reports 5:15029, 2015.
  • Mohit Jain, et al. A systematic survey of lipids across mouse tissues. AJP Endocrinology and Metabolism 306:8, E854-E868, 2014.
  • Roland Nilsson, Mohit Jain, Nikhil Madhusudhan, Nina Gustafsson Sheppard, Laura Strittmatter,et al. Metabolic enzyme expression highlights a key role for MTHFD2 and the mitochondrial folate pathway in cancer. Nature Communications 5:3128, 2014.
  • Sonia Sharma, et al. An siRNA screen for NFAT activation identifies septins as coordinators of store-operated Ca2+ entry. Nature 499:238-242, 2013.
  • Shohreh Maleki, Hanna M Björck, Lasse Folkersen, Roland Nilsson, Johan Renner, et al. Identification of a novel flow-mediated gene expression signature in patients with bicuspid aortic valve. Journal of Molecular Medicine 91:129–139, 2013.
  • Mohit Jain & Roland Nilsson, et al.
    Metabolite Profiling Identifies a Key Role for Glycine in Rapid Cancer Cell Proliferation. Science 336:1040-1044, 2012.
  • Vishal M. Gohil, Roland Nilsson, et al.
    Mitochondrial and nuclear genomic responses to loss of LRPPRC expression. Journal of Biological Chemistry 285:13742–13747, 2010.
  • Vishal M. Gohil & Sunil A. Sheth, Roland Nilsson, Andrew P. Wojtovich, Jeong H. Lee JH, et al.
    Nutrient-sensitized screening for drugs that shift energy metabolism from mitochondrial respiration to glycolysis. Nature Biotechnology 28:249–255, 2010.
  • Joshua M. Baughman, Roland Nilsson, et al. A Computational Screen for Regulators of Oxidative Phosphorylation Implicates SLIRP in Mitochondrial RNA Homeostasis. PLoS Genetics 5:8, 2009.
  • Roland Nilsson & Iman Schulz, Eric L. Pierce, Kathleen A. Soltis, Amornrat Naranuntaratet, et al. Discovery of Genes Essential for Heme Biosynthesis through Large-Scale Gene Expression Analysis. Cell Metabolism 10:2, 119–130, 2009.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity. The Journal of Machine Learning Research 10, 1071–1094, 2009.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. On reliable discovery of molecular signatures. BMC Bioinformatics 10:38, 2009.
  • Josefin Skogsberg, Andrea Dicker, Mikael Rydén, Gaby Åström, Roland Nilsson, et al. ApoB100-LDL Acts as a Metabolic Signal from Liver to Peripheral Fat Causing Inhibition of Lipolysis in Adipocytes. PLoS ONE 3:11, 2008.
  • Josefin Skogsberg, Jesper Lundström, Alexander Kovacs, Roland Nilsson, Peri Noori, et al. Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes. PLoS Genetics 4:3, 2008.
  • Alex Kovacs, Per Tornvall, Roland Nilsson, Jesper Tegnér, Anders Hamsten, et al. Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia. PNAS 104:34, 13768-13773, 2007.
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Consistent feature selection for pattern recognition in polynomial time. Journal of Machine Learning Research 8, pp. 589–612, 2007 .
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Detecting multivariate differential expression patterns. BMC Bioinformatics 8:150, 2007.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Towards scalable and data efficient learning of Markov boundaries. International Journal of Approximate Reasoning 45:2, 211–232, 2007.
  • Jesper Tegnér, Roland Nilsson, Vladimir B. Bajic, Johan Björkegren and Timothy Ravasi. Systems biology of innate immunity. Cellular Immunology 244:2, 105–109, 2006.
  • Roland Nilsson, Vladimir B. Bajic, Harukazu Suzuki, Diego di Bernardo, Johan Björkegren, et al. Transcriptional network dynamics in macrophage activation. Genomics 88:2, pp. 133–42, 2006.
  • Roland Nilsson, José M. Peña, Johan Björkegren and Jesper Tegnér. Evaluating feature selection for SVMs in high dimensions. In proc. of the 17th European Conference on Machine Learning, pp. 719–726, 2006.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Identifying relevant nodes without learning the model. In proc. of the 22nd Conference on Uncertainty in Artificial Intelligence, pp. 367–374, 2006.
  • José M. Peña, Roland Nilsson, Johan Björkegren and Jesper Tegnér. Reading dependencies from the minimal undirected independence map of a graphoid that satisfies weak transitivity. In proc. of the 3rd European Workshop on Probabilistic Graphical Models, pp. 247–254, 2006.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. A flexible implementation for support vector machines. The Mathematica Journal 10:1, pp. 114–127, 2006.
  • Roland Nilsson, Johan Björkegren and Jesper Tegnér. A powerful differential expression test for probe-level oligonucleotide microarray data. In proc. of the 2nd IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 10–14, 2004.