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Total result(s) found: 182
  • ​Neuronal methylation reveals CREB-asociated neuro-axonal impairment in Multiple Sclerosis
    L. Kular et al.
    Clinical Epigenetics, June 2019
Multiple sclerosis, Neurons, DNA methylation, DNA hydroxymethylation, Axonal guidance, Synaptic plasticity, CREB, Neurodegeneration
  • ​The thermodynamics of network coding, and an algorithmic refinement of the principle of maximum entropy
    H. Zenil, N.A. Kiani, J. Tegnér
    Entropy, 21(6), 560, 2019 
Second law of thermodynamics, Reprogrammability, Algorithmic complexity, Generative mechanisms, Deterministic systems, Algorithmic randomness
  • Algorithmic information dynamics of emergent, persistent, and colliding particles in the game of life
    H. Zenil, N.A. Kiani and J. Tegnér
    In A. Adamatzky (ed), From Parallel to Emergent Computing (book), Taylor & Francis / CRC Press,  pp.367-383, 2019

Kolmogorov-Chaitin complexity, Cellular automata, Algo-rithmic probability, Algorithmic Coding Theorem, Turing machines
  • Causal deconvolution by algorithmic generative models
    H. Zenil, N. Kiani,  A.A. Zea and J. Tegnér
    Nature Machine Intelligence, Jan 2019
Algorithmic generative models, Algorithmic probability, Algorithmic framework
  • Combining evidence from four immune cell types identifies DNA methylation patterns that implicate functionally distinct pathways during Multiple Sclerosis progression
    E. Ewing, L. Kular, S.J. Fernandes, N. Karathanasis, V. Lagani, S. Ruhrmann, I. Tsamardinos, J. Tegner, F. Piehl, D. Gomez-Cabrero, M. Jagodic
    EBioMedicine, 30 April 2019
450 K, DNA methylation, EPIC, Epigenetics, Immune cells, Multiple sclerosis
  • Feedforward regulation of Myc coordinates lineage-speciifc with housekeeping gene expression during B cell progenitor cell differentiation
    I. Ferreirós-Vidal, T. Carroll, (...) J. Tegner*, M. Merkenschlager*, D. Gomez-Cabrero
    PLoS Biology, March 2019 (*corresponding authors)
Cell differentiation, B cells, Gene expression, Gene regulation, Transcription factors, Cell metabolism
  • Phosphatase inhibitor PPP1R11 modulates resistance of human T cells toward Treg‐mediated suppression of cytokine expression
    R.N. Joshi, S.J. Fernandes, M.‐M. Shang, N.A. Kiani, D. Gomez‐Cabrero, J. Tegnér, A. Schmidt
    Journal of Leukocyte biology, 18 March 2019
CD4 T cell, T cell resistance, Immune suppression, Immunotherapy, Phosphatase, Regulatory T cell
  • MAPK pathways and B cells overactivation in multiple sclerosis revealed by phospoproteomics and genomic analysis 
    E. Kotelnikova, N.A. Kiani, (...), J. Tegner, P. Villoslada
    Proceedings of the National Academy of Sciences April 2019
Multiple sclerosis, Phosphoproteomics, Signaling pathways, B cells, Autoimmunity
  • Estimations of integrated information based on algorithmic complexity and dynamic querying
    A. Hernández-Espinosa, H. Zenil, N.A. Kiani, J. Tegnér
    arXiv preprint, accepted in Entropy arXiv:1904.10393, 2019

Integrated information, Algorithmic complexity, Algorithmicinformation theory, Algorithmic randomness
  • An algorithmic information calculus for causal discovery and reprogramming systems
    H. Zenil, N.A. Kiani, F. Marabita, Y. Deng, S. Elias, A. Schmidt, G. Ball, J. Tegnér
    bioaRXiv, In Press iScience, June 2019, Cell Press

Reprogramming systems
  • Controllability, multiplexing, and transfer learning in networks using evolutionary learning
    R. Ooi, C.-H. Huck Yang, P.-Y. Chen, V. Eguiluz, N. Kiani, H. Zenil, D. Gomez-Cabrero, J. Tegner
    arXiv, 2018
Controllability, Multiplexing, Evolutionary learning
  • ​Synthesizing new retinal symptom images by multiple generative models
    Y.-C. Liu, H.-H. Yang, C.-H. Huck Yang, J.-H. Huang, M. Tian, H. Morikawa, J. Tegner
    ACCV Workshop of AI in Retina Image Analysis, 2018
Age-Related Macular Degeneration (AMD), Machine learning, Generative Adversarial Networks (GANs)
  • ​Auto-classification of retinal diseases in the limit of sparse data using a two-streams machine learning model
    C.-H. Huck Yang, F. Liu, J.-H. Huang, M. Tian, H. Morikawa, I-H. Lin M.D., Y.C. Liu, H.-H. Yang, J. Tegner
    ACCV Workshop of AI in Retina Image Analysis, 2018
Machine learning, Retinal deseases, Vascular disorders, Auto classification, Support vector machine, Deep neural networks
  • ​A  novel  hybrid  machine  learning model  for  auto-classification  of  retinal  diseases
    C.-H. H. Yang, J.-H. Huang, F. Liu, F.-Y. Chiu, M. Gao, W. Lyu, J. Tegner, et all 
    ICML Workshop of Computational Biology, 2018
Machine learning, Retinal deseases, Auto classification, Support vector machine, Deep neural networks
  • ​Learning functions in large networks requires modularity and produces multi-agent dynamics
    C.H.H. Yang, R. Ooi, T. Hiscock, V. Eguiluz, J. Tegner
    ICML Workshop of Computational Biology, 2018
Large networks, Multi-agent dynamics, biological systems, Machine learning
  • DNA methylation as a mediator of HLA-DRB1* 15: 01 and a protective variant in multiple sclerosis
    L. Kular, Y. Liu, S. Ruhrmann, G. Zheleznyakova, F. Marabita, D. Gomez-Cabrero, T. James, E. Ewing, (…), Jesper Tegnér, at all
    Nature Communications 9 (1), 2397, 2018
HLA DRB1 antigen, DNA, Methylation, In vitro study, Multiple sclerosis
  • ​An algorithmic refinement of maxent induces a thermodynamic-like behaviour in the reprogrammability of generative mechanisms
    H. Zenil, N.A. Kiani, J. Tegnér
    arXiv preprint arXiv:1805.07166, 2018
Second law of thermodynamics, Reprogrammability, Algorith- mic complexity, Generative mechanisms
  • Building gene regulatory networks from single-cell ATAC-seq and RNA-seq using linked self-organizing maps
    C. Jansen, R. Ramirez, N. El-Ali, D. Gomez-Cabrero, J. Tegner, M. Merkenschlager, A. Conesa, A. Mortazavi
    bioRxiv, 438937, 2018
Single-cell, Gene regulatory networks, Self-organizing maps, RNA-seq
  • A decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity
    Zenil, Hernández-Orozco, Kiani, Soler-Toscano, Rueda-Toicen, Tegner
    Entropy 20 (605), 2018
Algorithmic probability, Algorithmic randomness, Information content, Information theory, Kolmogorov-Chaitin complexity, Shannon entropy, Thue-Morse sequence
  • ​Symmetry and correspondence of algorithmic complexity over geometric, spatial and topological representations
    H. Zenil, N.A. Kiani, J. Tegnér
    Entropy 20, 534, 2018
Algorithmic coding theorem, Algorithmic probability, Information content, Kolmogorov-Chaitin complexity, Molecular complexity, Polyhedral networks, Polyominoes, Polytopes, Recursive transformation, Shannon entropy, Symmetry breaking, Turing machines
  • A review of graph and network complexity from an algorithmic information perspective
    H. Zenil,N.A. Kiani, J. Tegnér
    Entropy 20 (551), 2018

Algorithmic information theory, Algorithmic probability, Algorithmic randomness, Biological networks, Complex networks, Kolmogorov-Chaitin complexity
  • ​Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation
    reveals novel regulators of FOXP3
    A. Schmidt, F. Marabita, N.A. Kiani, C.C. Gross, (...), H. Wiendl, R. Lahesmaa, and J. Tegnér
    BMC Biology2018
Regulatory T cells, Treg, iTreg, FOXP3, T cell differentiation, RNA sequencing (RNA-Seq), Data integration, TGF-β
  • ​Overexpression of endothelin B receptor in glioblastoma: a prognostic marker and therapeutic target?
    S. Vasaikar, G. Tsipras, N. Landázuri, H. Costa, (...), J. Tegner, K.-C. Yaiw, and C. Söderberg-Naucler
    BMC cancer 18 (1), 154
Glioblastoma, Endothelin B receptor, Endothelin receptor antagonists
  • ​Algorithmic complexity and reprogrammability of chemical structure networks
    H. Zenil, N.A. Kiani, M.M. Shang, J. Tegnér
    Parallel Processing Letters 28 (01), 1850005
Algorithmic information theory, Algorithmic probability, Causal path, Causality, Chemical compound complexity, Information signature, Kolmogorov-Chaitin complexity, Molecular complexity, Shannon entropy
  • Symmetry and algorithmic complexity of polyominoes and polyhedral graphs
    H. Zenil, N.A. Kiani, J. Tegnér
    arXiv preprint arXiv:1803.02186
Algorithmic symmetry, Geometric symmetry, Kolmogorov-Chaitin complexity
  • ​Ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism
    H. Zenil, N.A. Kiani, J Tegnér
    arXiv preprint arXiv:1802.09904
Complex data, Algorithmic Probability
  • Unsupervised and universal data reduction and network sparsification methods by minimal algorithmic information loss
    H. Zenil, N.A. Kiani, J. Tegnér
    arXiv preprint arXiv:1802.05843
Data reduction, Network sparsification, Algorithmic information theory
  • Predictive systems toxicology
    N.A. Kiani, M.M. Shang, H. Zenil, J. Tegnér
    Computational Toxicology, 535-557​, 2018
Toxicity, Machine learning techniques, Molecular mechanisms
  • Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients
    T. James, M. Lindén, H. Morikawa, S.J. Fernandes, S. Ruhrmann, M. Huss, M. Brandi, F. Piehl, M. Jagodic, J. Tegnér, M. Khademi, T. Olsson, D. Gomez-Cabrero, I. Kockum
    Human Molecular Genetics, Volume 27, Issue 5, Pages 912–928, 2018
Kappa-B activation, RNA-SEQ data, Functional variation, Disease, Variants, Annotation, Genome, Association, Autoimmune, Leukocytes
  • ​The ultra-sensitive Nodewalk technique identifies stochastic from virtual, population-based enhancer hubs regulating MYC in 3D: Implications for the fitness of cancer cells
    N. Sumida, E. Sifakis, B.A. Scholz, A.F. Woodbridge, N. Kiani, D. Gomez-Cabrero, J.P. Svensson, J. Tegner, A. Gondor, R. Ohlsson
    bioRxiv, 286583, 2018
Stochastic transcriptional bursts, Dynamic 3D chromatin states, Ultra-sensitive Nodewalk technique, Cancer cells
  • ​Phosphoproteomics reveals regulatory T cell-Mediated DeF6 dephosphorylation that affects cytokine expression in human conventional T cells
    R.N. Joshi, N.A. Binai, F. Marabita, Z. Sui, A. Altman, A.J.R. Heck, J. Tegnér, A. Schmidt
    Frontiers in immunology 8, 1163, 2017
Regulatory T cell, Treg, CD4 T cell, Phosphoproteomics, DEF6, SLAT, NFAT, TCR signaling
  • Dynamics and heterogeneity of brain damage in multiple sclerosis
    E. Kotelnikova, N.A. Kiani, E. Abad, E.H. Martinez-Lapiscina, M. Andorra, I. Zubizarreta, I. Pulido-Valdeolivas, I. Pertsovskaya, L.G. Alexopoulos, T. Olsson, R. Martin, F. Paul, J. Tegnér, J. Garcia-Ojalvo, P. Villoslada
    PLoS computational biology 13 (10), 2017
Central-nervous-system, Demyelinating diseases, Pathology, Multiple sclerosis, Neurodegeneration, Remyelination, Pathogenesis, Inflammation
  • Guidelines for developing successful short advanced courses in systems medicine and systems biology
    D. Gomez-Cabrero, F. Marabita, S. Tarazona, I. Cano, J. Roca, A. Conesa, P. Sabatier, J. Tegnér
    Cell Systems, In press, 2017
Systems medicine, Systems biology
  • ​Hypermethylation of MIR21 in CD4+ T cells from patients with relapsing-remitting multiple sclerosis associates with lower miRNA-21 levels and concomitant up-regulation of its target genes
    S. Ruhrmann, E. Ewing, E. Piket, L. Kular, J.C.C. Lorenzi, (...), J. Tégner, D. Gomez-Cabrero, F. Piehl, M. Jagodic
    Multiple Sclerosis Journal, 1352458517721356, 2017
CD4+ T cells, DNA methylation, Autoimmunity, Epigenetics, MicroRNAs, Multiple sclerosis, Relapsing-remitting
  • ​Low-algorithmic-complexity entropy-deceiving graphs
    H. Zenil, N.A. Kiani, J. Tegnér
    Phys. Rev. E 96, 012308, 2017
Computational complexity, Algorithmic complexity, Entropy rates, Information theoretic measure
  • ​Omic signatures in frailty and frailty diagnosis
    D. Gomez-Cabrero, S. Walter, I. Abugessaisa, L. Rodríguez-Mañas, J. Tégner
    Innovation in Aging, Volume 1, Issue suppl_1, Pages 903, 2017
Omic signatures
  • ​HiDi: An efficient reverse engineering schema for large scale dynamic regulatory network reconstruction using adaptive differentiation
    Y. Deng, H. Zenil, J. Tégner, N.A. Kiani
    ArXiv preprint, arXiv:1706.01241
Adaptive differentiation, Reverse engineering
  • ​Viva Europa, a land of excellence in research and innovation for health and wellbeing
    C. Auffray, M. Sagner, S. Abdelhak, I. Adcock, A. Agusti, M. Amaral, ... J. Tegnér at al...
    Progress in Preventive Medicine 2 (3), e006, 2017
Europe, Research
  • ​Editorial overview for the thematic issue on Clinical and translational systems biology
    J. Tegnér, D. Gomez-Cabrero
    Current opinion in systems biology, Volume 3, Pages xii-xiv, 2017
Living systems
  • ​SCENERY: a web application for (causal) network reconstruction from cytometry data
    G. Papoutsoglou, G. Athineou, V. Lagani, I. Xanthopoulos, A. Schmidt, S. Éliás, J. Tegnér, I. Tsamardinos
    Nucleic Acids Research, gkx448, 2017
Biological markers; Cells; Multimedia; Reconstructive surgical procedures; Machine learning
  • ​Comment on" Epigenetics in the pathogenesis of RA".
    D. Gomez-Cabrero, J. Tegnér, T.J. Ekström, C. Ospelt
    Seminars in immunopathology, pp 1–2, 2017
Rheumatoid arthritis; Autoimmune disorders; Immunopathology
  • ​Iterative systems biology for medicine – time for advancing from network signature to mechanistic equations
    D. Gomez-Cabrero, J. Tegnér
    Current Opinion in Systems Biology, Volume 3, Pages 111–118, 2017
Systems medicine; Kitano; Mathematical modeling; Causality
  • ​Predicting causal relationships from biological data: applying automated casual discovery on mass cytometry data of human immune cells
    S. Triantafillou, V. Lagani, C. Heinze-Deml, A. Schmidt, J. Tegner, I. Tsamardinos
    Scientific Reports 7, Article number: 12724, 2017
Biological data; Human immune cells; Molecular system; Immune system; Systems biology
  • The information theoretic and algorithmic approach to human, animal, and artificial cognition
    N. Gauvit, H. Zenil, J. Tegnér
    Representation and Reality: Humans, Animals and Machines”, Springer Verlag, 2015
Theoretic approach; Algorithmic approach, Human, Animal, Artificial cognition
  • ​Causality, Information and Biological computation: an algorithmic software approach to life, disease and the immune system
    H. Zenil, A. Schmidt, J. Tegnér
    Contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Press, 2017
Causality; Information; Biological Computation; Disease; Immune system
  • A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis
    V. Kannan, N.A. Kiani, F. Piehl, and J. Tegnér
    Mathematical Biosciences, 2017
Multiple sclerosis; Disease trajectories; Disease progression; Central nervous system
  • ​Functional genomics analysis of vitamin D effects on CD4+ T-cells in vivo in experimental autoimmune encephalomyelitis
    M. Zeitelhofera, M.Z. Adzemovica, D. Gomez-Cabreroc, P. Bergmana, S. Hochmeisterf, M. N’diayea, A. Paulsona, S. Ruhrmanna, M. Almgrena, J. Tegnér, T.J. Ekströma, A.O. Guerreiro-Cacaisa, and M. Jagodica
    PNAS Volume 114, Issue 9, Pages E1678-E1687, 2017
DNA methylation; Epigenetics; Experimental autoimmune encephalomyelitis; Multiple sclerosis; Vitamin D
  • ​Quantifying loss of information in network-based dimensionality reduction techniques
    H. Zenil, N. Kiani, J.Tegnér
    Journal of Complex Networks, Volume 4, Issue 3, Pages 342-362, 2016
Dimensionality reduction techniques; Graph motifs; Graph sparsification; Graph spectra; kolmogorov complexity
  • ​Network based drug repositioning methodology for neurodegenerative diseases
    M.-M. Shang, N. Kiani, J. Tegnér
    International Work Conference on Bioinformatics and Bioengineering, IWBBIO 2016
Biological Network analisys; Neurodegenerative disease; Drug repositioning; Flow cytometry; Statin
  • ​Systems Toxicology: systematic approach to predict toxicity
    N. Kiani, Ming-Mei Shang, J. Tegnér
    Curr Pharm Des., 22(46):6911-6917, 2016
Drug adverse effects; Network analysis; Predictive modeling; System pharmacology
  • ​TGF-β affects the differentiation of human GM-CSF+ CD4+ T cells in an activation- and sodium-dependent manner
    S. Éliás, A. Schmidt, V. Kannan, J. Andersson, J. Tegnér
    Frontiers in Immunology, 7:603, 2016
Autoimmune diseases; Differentiation; GM-CSF; Human CD4+ T cells; Multiple sclerosis; Multivariate analysis; Sodium; TGF-β
  • ​In viotro differentiation of human CD4+FOXP3+ induced regulatory T cells (iTregs) from Naïve CD4+ T cells using a TGF-β-containing protocol
    A. Schmidt, E. Szabolcs, R. Joshi, and J. Tegnér
    J Vis Exp (118). 2016
Immunology; Regulatory T cells; Treg, CD4+ T cells; Magnetic cell isolation; In vitro differentiation; Cytokines; FOXP3; TGF-β; IL-2; Intracellular Flow cytometry; qRT-PCR
  • ​A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference
    J. Tegnér, H. Zenil, N. Kiani, G. Ball, D. Gomez-Cabrero
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences: Volume 374, Issue 2080, Article number 20160144, 2016
Big data; Computational biology; Living systems; Model reduction; Modelling; Systems biology
  • ​Human cytomegalovirus may promote tumour progression by upregulating arginase-2
    X. Xu, H. Costa, G. Overbeek, S. Vasaikar, C. Pawan K. Patro, G. Shafi, S. Ananthaseshan, G. Tsipras, B. Davoudi, A.-A. Mohammad, H. Lam, K. Straat, J. Tegnér, J.C. Tong, K.T. Wong, C. Söderberg-Naucler, and K.-C. Yaiw
    Oncotarget, 7(30):47221-47231, 2016
Arginase; Cytomegalovirus; Glioblastoma; Treatment
  • ​Conditional disease development extracted from longitudinal health care cohort data using layered network construction
    V. Kannan, F. Swartz, I. Abugessaisa, N.A. Kiani, G. Silberberg, G. Tsipras, D. Gomez-Cabrero, K. Alexanderson, and J. Tegnér
    Scientific Reports 6, Article number: 26170, 2016
Layered network construction; Conditional disease; Health care data
  • ​Adaptive input data transformation for improved network reconstruction with information theoretic algorithms
    V. Kannan, and J. Tegnér
    Stat. Appl. Genet. Mol. Biol., 1;15(6):507-520, 2016
Algoithms; Mutual information; Numerical estimation
  • ​High-specificity bioinformatics framework for epigenomic profiling of discordant twins reveals specific and shared novel markers for ACPA and ACPA-positive rheumatoid arthritis
    D. Gomez-Cabrero, M. Almgren, L.K. Sjöholm, A. Hensvold, R. Tryggvadottir, J. Kere, A. Scheynius, N. Acevedo, L. Reinius, M.A. Taub, C. Montano, M.J. Aryee, J.I. Feinberg, A.P. Feinberg, J. Tegnér, L. Klareskog, A.I. Catrina, and T.J. Ekström
    Genome Medicine, Volume 8, Issue 1, Article number 124, 2016
ACPA; Bioinformatics; DNA methylation; Epigenetics; Rheumatoid arthritis
  • ​From comorbidities of chronic obstructive pulmonary disease to identification of shared molecular mechanisms by data integration
    D. Gomez-Cabrero, J. Menche, C. Vargas, I. Cano, D. Maier, A.L. Barabási, J. Tegnér J. Roca
    BMC Bioinformatics 17 (15), 441, 2016
Associated mechanism; Chronic obstructive pulmonary disease; Disease initiations; Generative mechanism; Molecular mechanism
  • ​Comparative analysis of protocols to induce human CD4+Foxp3+regulatory T cells by combinations of IL-2, TGF-beta, retinoic acid, rapamycin and butyrate
    A. Schmidt, M. Eriksson, M.-M. Shang, H. Weyd, and J. Tegnér
    PLoS One, Volume 11, Issue 2, Article number e0148474, 2016
Animal experiment; CD4+ Foxp3+ regulatory T cell; Cell isolation; Comparative study; Controlled study
  • ​Methods of information theory and algorithmic complexity for network biology
    H. Zenil, N. Kiani, J. Tegnér
    Seminars of Developmental and Cell Biology March 51: 32-43, 2016
Algorithmic probability; Algorithmic randomness; Biological networks; Complex networks; Information theory; Kolmogorov complexity
  • Evaluating network inference methods in preserving the topology and complexity of reconstructed genetic networks
    N. Kiani, H. Zenil, J. Olczak, J. Tegnér
    Seminars of Developmental and Cell Biology March 51 :44-52, 2016
Network reverse engineering; Network reconstruction; Evaluation of networks; Information content; Shannon entropy; Algorithmic complexity
  • Bioinformatics mining and modeling methods for the identification of disease mechanisms in neurodegenerative disorders
    M. Hofmann-Apitius, G. Ball, S. Gebel, S. Bagewadi, B. de Bono, R. Schneider, M. Page, A. Tom Kodamullil, E. Younesi, C. Ebeling, J. Tegnér and L. Canard
    International journal of molecular science, 16(12) 29179-29206, 2016 
Bioinformatics; Data integration; Disease models; Genetics; Graphical models; Knowledge-based modeling; Mechanism-identification; Multiscale; Neurodegeneration
  • ​Health risk assessment and stratification in an integrated care scenario
    M. Moharra, E. Vela, I. Dueñas-Espín, S. Pauws, C. Bescos, I. Cano, J. Tegnér, at all.
    International Journal of Integrated Care, 16(6): A322, pp. 1-8, 2016
Case finding, Clinical decision making, Chronic care, Health risk assessment, Patient stratification
  • ​Human macrophages induce CD4 + Foxp3 + regulatory T cells via binding and re-release of TGF-β
    A. Schmidt, X.-M. Zhang, R.N. Joshi, S. Iqbal, C. Wahlund, S. Gabrielsson, R.A. Harris and J. Tegnér
    Immunology and Cell Biology 94, 747-762, 2016
Adoptive transfer; CD4+ T lymphocyte; Cell differentiation; Controlled study; Cytokine production
  • Inferring causal molecular networks: empirical assessment through a community-based effort
    S. Hill, et al.
    Nature Methods 13, 310–318, 2016
Algorithms; Causality; Computational Biology; Computer Simulation; Gene Expression Profiling; Gene Regulatory Networks; Humans
  • Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR
    F. Marabita, P. de Candia, A. Torri, J. Tegnér, S. Abrignani, R.L. Rossi
    Briefings Bioinformatics, 204-212, 2016
Normfinder; Circulating miRNA; GeNorm; Normalization; qPCR; Reference genes
  • ​Optimization in biology parameter estimation and the associated optimization problem
    G. Cedersund, O. Samuelsson, G. Ball, J. Tegnér, D. Gomez-Cabrero
    Book chapter in Uncertainty in Biology, Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 177-197, 2016
Parameter estimation; Optimization; Heuristic; Fitness function
  • Computational modeling under uncertainty: challenges and opportunities
    D. Gomez-Cabrero, J. Tegnér, L. Geris
    Book chapter in Uncertainty in Biology, Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 467-476, 2016
Computational modeling; Uncertainty; Challenges; HPC; Hypothesis generation
  • Neuroswarm: a methodology to explore the constraints that function imposes on simulation parameters in large-scale networks of biological neurons
    D. Gomez-Cabrero, S. Ardid, M. Cano-Colino, J. Tegnér, A. Compte
    Book chapter in Uncertainty in Biology, Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 427-447, 2016
Prefrontal cortex; Workflow; Ensemble analysis; Working memory model; Neuroscience; Computational biology
  • Modeling and model simplification to facilitate biological insights and predictions
    O. Eriksson, J. Tegnér
    Book chapter in Uncertainty in Biology, Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 301-325, 2016
Model simplificaton; Model reduction; Data integration; Dynamical models; Ordinary differential equations; Piecewise linear; Dynamical modules
  • ​Probabilistic computational causal discovery for systems biology
    V. Lagani, S. Triantafillou, G. Ball, J. Tegnér, I. Tsamardinos
    Book chapter in Uncertainty in Biology, Volume 17 of the series Studies in Mechanobiology, Tissue Engineering and Biomaterials pp 33-73, 2016
Causality; Causal graphical models; Bayesian networks; Systems biology; Biological networks
  • Systems understanding to personalized medicine - lessons and recommendations based on a multi-disciplinary and translational analysis of COPD
    J. Roca, D. Gomez-Cabrero, J. Tegnér
    Systems Biology for Medicine, Springer series "Methods in Molecular Biology"- 283-303, 2016
Clinical decision support; Integrated care; Comorbidity; Disease modeling; Knowledge management
  • ​Proposals for enhanced health risk assessment and stratification in an integrated care scenario
    I. Dueñas-Espín, E. Vela, S. Pauws, C. Bescos, I. Cano, M. Cleries, J.C. Contel, E. de Manuel Keenoy, J. Garcia-Aymerich, D. Gomez-Cabrero, R. Kaye, M.M.H. Lahr, M. Lluch-Ariet, M. Moharra, D. Monterde, J. Mora, M. Nalin, A. Pavlickova, J. Piera, S. Ponce, S. Santaeugenia, H. Schonenberg, S. Störk, J. Tegnér, F. Velickovski, C. Westerteicher, J. Roca
    BMJ Open, Volume 6, Issue 4, 2016
Human experiment; Information processing; Morbidity; Population model; Prediction; Risk assessment; Scotland; Stratification; Telehealth
  • ​Human regulatory T cells rapidly rewire the phosphoproteome of suppressed conventional T cells
    R.N. Joshi, N. Binai, F. Marabita, J. Tegnér, A. Schmidt
    Scandinavian Journal of Immunology 83 (5), 376-377, 2016
Regulatory T cells; Phosphoproteome; Conventional T cells
  • Integrative molecular profiling during human induced regulatory T cell (itreg) generation reveals novel regulators of Foxp3
    A. Schmidt, F. Marabita, H. Johansson, J. Lehtiö, M. Eriksson, S. Éliás, D. Gomez-Cabrero, A. Rao, J. Tegnér
    Scandinavian Journal of Immunology 83 (5), 353, 2016
Molecular profiling, human induced regulatory T cell (itreg)
  • Characterization and mechanistic dissection of the differentiation of Gm-csf+ Cd4+ T cells
    S. Éliás, A. Schmidt, V. Kannan, J. Andersson, J. Tegnér
    Scandinavian Journal of Immunology 83 (5), 354-355, 2016
Gm-csf+ Cd4+ T cells
  • Topological evaluation of methods for reconstruction of genetic regulatory networks
    J. Olczak, N. Kiani, H. Zenil, J. Tegnér
    Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015, Article number 7400604, Pages 468-473, 2016
Evaluating measure; Inference methods evaluation; Network reconstruction; Topological properties
  • ​Monozygotic twins discordant for common variable immunodeficiency reveal impaired DNA demethylation during naïve-to-memory B-cell transition
    V. Rodríguez-Cortez, L. Pino-Molina, J. Rodríguez-Ubreva, L. Ciudad, D. Gómez-Cabrero, C. Company, J. Urquiza, J. Tegnér, C. Rodríguez-Gallego, E. López-Granados, E. Ballestar
    Nature Communications, Volume 6, Article number 7335, 2015
DNA; Gene expression; Germ cell; Memory; Methylation
  • ​VEGF-B promotes cancer metastasis through a VEGF-A–independent mechanism and serves as a marker of poor prognosis for cancer patients
    X. Yang, Y. Zhang, K. Hosaka, P. Andersson, J. Wang, F. Tholander, Z. Cao, H. Morikawa, J. Tegnér, Y. Yang, H. Iwamoto, S. Lim, Y. Cao
    Proceedings of National Academy of Science 5/19, 2015
Angiogenesis; Metastasis; VEGF-A; VEGF-B; VEGFR1
  • ​IL-1β promotes Th17 differentiation by inducing alternative splicing of FOXP3
    R.K.W. Mailer, A.-L. Joly, S. Liu, S. Elias, J. Tegnér, J. Andersson
    Scientific Reports, Volume 5, Article number 14674, 2015
Crohn's disease; Interleukins; Peripheral tolerance; Transcription
  • ​The folate-coupled enzyme MTHFD2 is a nuclear protein and promotes cell proliferation
    Reiner K.W. Mailer, A.-L. Joly, S. Liu, Sz. Elias, J. Tegnér, J. Andersson
    Scientific Reports, Volume 5, Article number 15029, 2015
Cell proliferation, Cancer metabolism; Computational models
  • Laboratory biomarkers and frailty: presentation of the frailomic initiative
    G. Lippi, P. Jansen-Duerr, J. Viña, A. Durrance-Bagale, I. Abugessaisa, D. Gomez-Cabrero, J. Tegnér, J. Grillari, J. Erusalimsky, A. Sinclair, L. Rodriguez-Manãs, FRAILOMIC consortium
    Clinical Chemistry and Laboratory Medicine, 1;53(10): e253-5 2015
Ageing; Biomarkers; Frailty; Sarcopenia
  • ​Signaling networks in MS: A systems-based approach to developing new pharmacological therapies
    E. Kotelnikova, M. Bernardo-Faura, G. Silberberg, N.A. Kiani, D. Messinis, I.N. Melas, L. Artigas, E. Schwartz, I. Mazo, M. Masso, L.G. Alexopoulos, J. Manuel Mas, T. Olsson, J. Tegnér, R. Martin, A. Zamora, F. Paul, J. Saez-Rodriguez, P. Villoslada
    Mult. Scler. 21(2):138-46, 2015
Drug discovery; Multiple sclerosis; Pathways; Signaling; Systems biology
  • ​DNA methylation: an epigenetic marker of breast cancer influenced by nutrients acting as an environmental factor
    T.N. Hasan, G. Shafi, B. Leena Grace, J. Tegnér, A. Munshi
    Chapter in Noninvasive Molecular Markers in Gynecologic Cancers, 191-210, CRC Press, 2015
DNA; DNA Methylation; Epigenetics; Breast canser
  • ​Introduction to data types in epigenomics
    F. Marabita, J. Tegnér, D. Gomez-Cabrero
    Chapter in Computational and Statistical Epigenomics, pp3-34, Springer, 2015
Epigenomics DNA; Methylation; Histone modifications; ChIP-seq; Bioinformatics
  • ​Numerical investigation of graph spectra and information interpretability of eigenvalues
    H. Zenil, N.A. Kiani, J. Tegnér
    Bioinformatics and Biomedical Engineering, Lecture Notes in Bioinformatics, 395-405, 2015
Algorithmic complexity; Algorithmic probability; Eigenvalues meaning; Graph spectra behavior; Information content; Network science
  • ​Approximations of algorithmic and structural complexity validate cognitive-behavioural experimental results
    H. Zenil, J.A.R. Marshall, J. Tegnér
    arXiv:1509.06338, 2015
Behavioural biases; Ant behaviour; Mouse behaviour; Drosophila behaviour; Communication complexity; Tradeoffs of complexity measures; Shannon Entropy; Kolmogorov-Chaitin complexity; Logical Depth
  • An integrative analysis reveals coordinated reprogramming of the epigenome in human skeletal muscle after training
    M.E. Lindholm, F. Marabita, D. Gomez-Cabrero, H. Rundqvist, T.J. Ekström, J. Tegnér, C.J. Sundberg
    Epigenetics, 9(12):1557-69, 2014
450k arrays; DNA methylation; Environment; Epigenomics; Exercise; Integrative analysis; Lifestyle; Transcriptional network
  • Accelerating translational research by clinically driven development of an informatics platform – a case study
    A. Imad, S. Saevarsdottir, G. Tsipras, S. Lindblad, C. Sandin, P. Nikamo, M. Ståhle, V. Malmström, L. Klareskog, and J. Tegnér
    PLoS One, V. 9, I. 9, Article number e104382, 2014
Arthritis, Rheumatoid; Autoantibodies; Biological Markers; Decision Support Systems, Gene Frequency; Polymorphism, Single Nucleotide
  • Data integration in the era of omics: current and future challenges
    D. Gomez-Cabrero, I. Abugessaisa, D. Maier, A. Teschendorff, M. Merkenschlager, A. Gisel, E. Ballestar, E. Bongcam-Rudloff, A. Conesa and J. Tegnér
    BMC Systems Biology, Vol. 8 (Supp 2):I1), 2014
Biological Science; Computational Biology; Data Collection; Data integration
  • Biomedical research in a Digital Health Framework
    I. Cano, M. Lluch-Ariet, D. Gomez-Cabrero, D. Maier, S. Kalko, M. Cascante, J. Tegnér, F. Miralles, D. Herrera, J. Roca
    J. Transl. Med. 12(Suppl 2): S10, 2014
Decision Support Systems, Program Development; Pulmonary Disease, Chronic Obstructive
  • ​Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management
    J. Roca, C. Vargas, I. Cano, V. Selivanov, E. Barreiro, (...), J. Tegnér, J. Escarrabill, A. Agustí, D. Gomez-Cabrero
    Journal of Translational Medicine, 12(Suppl 2):S3, 2014
Cluster Analysis; Gene Expression Profiling; Lung Diseases; Lung Injury; Oxygen Consumption; Pulmonary Disease
  • Systems Medicine: from molecular features and models to the clinic in COPD
    D. Gomez-Cabrero, I. Cano, I. Abugessaisa, M. Huertas-Migueláñez, (...), J. Tegnér
    Journal of Translational Medicine, 12(Suppl 2):S4, 2014
Biomarkers; Comorbidity; Computer Simulation; Decision Support Systems, Energy Metabolism; Reactive Oxygen Species; Translational Medical Research
  • Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project
    F. Miralles, D. Gomez-Cabrero, M. Lluch-Ariet, J. Tegnér, M. Cascante, J. Roca
    Journal of Translational Medicine, 12(Suppl 2):S12, 2014
Chronic disease; Cluster analysis; Lung diseases; Pulmonary medicine; Risk assessment; Translational medical research
  • ​Synergy-COPD: a systems approach for understanding and managing chronic diseases
    D. Gomez-Cabrero, Magi Lluch-Ariet, J. Tegnér, M. Cascante, F. Miralles, J. Roca
    Journal of Translational Medicine, 12(Suppl 2):S2, 2014
Computer Simulation; Decision Support Systems; Delivery of Health Care; Electronic Health Records; Program Development
  • Oxygen pathway modeling estimates high reactive oxygen species production above the highest permanent human habitation
    I. Cano, V. Selivanov, D. Gomez-Cabrero, J. Tegnér, J. Roca, P.D. Wagner, M. Cascante
    PLoS One, Volume 9, Issue 11, Article number 111068, 2014
Altitude; Computer Simulation; Mitochondria, Models, Oxidative Stress; Oxygen
  • ​Methylome characterization of CD4+ T cells in multiple sclerosis — Establishing a role for miR-21 in autoimmune disease
    S. Ruhrmann, E. Piket, P. Bergman, L. Kular, J. Cesar, C. Lorenzi, S. Aeinehband, R. Parsa, D. Gomez-Cabrero, J. Tegnér, F. Piehl, M. Jagodic
    Journal of Neuroimmunology, Volume 275, Issues 1-2, Page 112, 2014
T cells; Multiple sclerosis; Autoimmune disease
  • Non-HLA genes PTPN22, CDK6 and PADI4 are associated with specific autoantibodies in HLA-defined subgroups of rheumatoid arthritis
    O. Snir, D. Gomez-Cabrero, A. Montes, E. Perez-Pampin, (...), J. Tegnér, A. Gonzalez, V. Malmström, L. Padyukov
    Arthritis Research & Therapy, 16:414, 2014
Autoantigens; Cyclin-Dependent Kinase 6; Enzyme-Linked Immunosorbent Assay; Genetic Predisposition to Disease; Genotype; HLA-DRB1 Chains
  • On the Theory and Algorithm for rigorous discretization in applications of Information Theory
    V. Kannan, J. Tegnér
    arXiv:1406.5104, 2014
Information Theory; Algorithmic probability
  • Molecular foundations of computational biomedicine
J. Tegnér, I. Abugessaisa, D. Gomez-Cabrero
    Chapter in Computational biomedicine, 6-32, 201
Computational bioscience; Nucleic acid; Protein; Computational modeling
  • ​Breast cancer MicroRNAs: clinical biomarkers for the diagnosis and treatment strategies
    G. Shafi, T.N. Hasan ,N.A. Syed , A. Paine, J. Tegnér, and A. Munshi
    Chapter in Omics Approaches in Breast Cancer, pp 171-182, Springer, 2014
Breast cancer; Breast cancer staging; Diagnosis; MicroRNA; Prognosis; Treatment strategies
  • ​Network biology empowering detection and understanding of interactions between genetic factors in development of complex phenotypes
    J. Tegnér, F. Marabita, D. Gomez-Cabrero
    Chapter in Between the Lines of Genetic Code, 175-194, 2014
Big Data; Computing; Genetic interactions; Network biology; Prediction; Systems biology
  • ​STATegra EMS: an Experiment Management System for complex next-generation omics experiments
    R. Hernández-de-Diego, N. Boix-Chova, D. Gómez-Cabrero, J. Tegnér, I. Abugessaisa and A. Conesa
    BMC Systems Biology 8(Suppl 2):S9, 2014
Metabolomics; Proteomics; High throughput sequencing; Human; Metabolomics
  • ​Integrative approaches to computational biomedicine
    P.V. Coveney, V. Diaz-Zuccarini, N. Graf, P. Hunter, P. Kohl, J. Tegner, M. Viceconti
    Interface Focus 3 (2), 20130003, 2013
Computational biomedicine; To human health; Virtual physiological human
  • ​An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation 450 BeadChip platform
    F. Marabita, M. Almgren, M.E. Lindholm, S. Ruhrmann, F. Fagerström-Billai, M. Jagodic, C.J. Sundberg, T.J. Ekström, A.E. Teschendorff, J. Tegnér and D. Gomez-Cabrero
    Epigenetics 8(3), 333–46, 2013
DNA methylation; Illumina 450K; Microarray; Normalization; Technical variability
  • Implementation of the CDC translational informatics platform - from genetic variants to the national Swedish Rheumatology Quality Register
    I. Abugessaisa, D. Gomez-Cabrero, O. Snir, S. Lindblad, L. Klareskog, V. Malmström, J. Tegnér
    Journal of Translational Medicine, 11:85. 11(1), 85, 2013
Patient de-identification; Secondary use of clinical data; Swedish Rheumatology Quality Register (SRQ)
  • ​Pediatric systems medicine: evaluating needs and opportunities using congenital heart block as a case study
    J. Tegnér, I. Abugessaisa
    Pediatric Research, 73, 508–513, 2013
Computational Biology; Genomics; Heart Block; Newborn; Pediatrics; Risk Factors
  • ​A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinum 450k DNA methylation data
    A.E. Teschendorff, F. Marabita, M. Lechner, T. Bartlett, J. Tegnér, D. Gomez-Cabrero, S. Beck
    Bioinformatics; 29(2):189-96, 2013
Algorithms; DNA methylation; Neoplasms; Normal distribution; Nucleic acid probes; Oligonucleotide array sequence analysis
  • Identification of novel markers in rheumatiod arthritis through integrated analysis of DNA methylation and microRNA expression
    L. de la Rica, J.M. Urquiza, D. Gómez-Cabrero, A.B. Islam, N. López-Bigas, J. Tegnér, R.E. Toes, E. Ballestar
    J. Autoimmunity, 41:6-16, 2013
Rheumatoid arthritis; Rheumatoid arthritis synovial fibroblasts; DNA methylation; Epigenetic; MicroRNAs
  • Algorithmic complexity of motifs clusters superfamilies of networks
    H. Zenil, N.A. Kiani, J. Tegnér
    Bioinformatics and Biomedicine (BIBM), IEEE, 74-76, 2013
Algorithmic probability; Complex networks; Information content; Information theory; Kolmogorov complexity; Network motifs; Network typology
  • ​Importance of mitochondrial PO2 in maximal O2 transport and utilization: A theoretical analysis
    I. Cano, M. Mickael, D. Gomez-Cabrero, J. Tegnér, J. Roca, P.D. Wagner
    Respiratory  Physiology & Neurobiology, Pages 477–483, 2013
Bioenergetics; Mitochondrial PO2; Mitochondrial respiration; Oxygen transport; V̇O2max
  • Testing biological models for non-linear sensitivity with a programmability test
    H. Zenil, G. Ball and J. Tegnér
    Advances in Artificial Life,12, ECAL, 2013
Biological models; Non-linear sensitivity; Programmability test
  • A vision and strategy for the virtual physiological human: 2012 update
    P. Hunter, T. Chapman, P.V. Coveney, B. de Bono, V. Diaz, J. Fenner, A.F. Frangi, P. Harris, R. Hose, P. Kohl, P. Lawford, K. McCormack, M. Mendes, S. Omholt, A. Quarteroni, N. Shublaq, J. Skår, K. Stroetmann, J. Tegnér, (...) and M. Viceconti
    Interface Focus, Vol. 3 No. 220130004, 2013
Computational physiology; Multiscale modelling; Physiome; Systems biology; Virtual physiological human
  • Identifying the relevant nodes without learning the model
    J.M. Pena, R. Nilsson, J. Björkegren, J. Tegnér
    arXiv preprint arXiv:1206.6847, 2012
Relevant nodes; Bayesian network; Databases
  • ​Pre-B cell to macrophage transdifferentiation without significant promoter DNA methylation changes
    J. Rodríguez-Ubreva, L. Ciudad, D. Gómez-Cabrero, M. Parra, L.H. Bussmann, A. di Tullio, J. Tegnér, T. Graf, and E. Ballestar
    Nucleic Acids Res. 40(5):1954-68, 2012
Cell transdifferentiation; Cells; DNA methylation; Epigenesis; B-Lymphoid; Promoter regions
  • ​Data integration: towards understanding biological complexity
    D. Gomez-Cabrero, J. Tegner
    Chapter 4:. Handbook of Statistical Systems Biology. Editors Stumpf, Balding and Girolami. John Wiley and Sons, 2011
Data integration; Biological entities; Knowledge Databases and Ontologies; Open Biomedical Ontologies (OBOs); KDs and Ontologies; Clustering algorithms; Gene-set analysis
  • ​Workflow for generating competing hypothesis from models with parameter uncertainty
    D. Gomez-Cabrero, A. Compte and J. Tegnér
    Interface Focus, Vol 1 No3, 438-449, 2011
Bioinformatics; Computational biology; Systems biology
  • ​Decoding complex biological networks – tracing essential and modulatory parameter combinations in complex and simplified models of the cell cycle
    O. Eriksson, T. Andersson, Y. Zhou, and J. Tegnér
    BMC Systems Biology, 7:5(1): 123, 2011
Complex biological networks; Cell cycle; Cell division; Mathematical models
  • ParkDB: a Parkinson´s disease gene expression database
    C. Taccioli, J. Tegnér, V. Maselli, D. Gomez-Cabrero, G. Altobelli, W. Emmett, F. Lescai, S. Gustincich, E. Stupka
    Database, Nucleic Acid Research, Oxford; bar 007, 2011
Databases, Gene expression regulation; Molecular sequence annotation; Parkinson disease
  • ​Epigenetic alterations in autoimmune disease
    E. Ballestar, B.M. Javierre, L. de la Rica, D. Gómez-Cabrero, J. Tegnér, C. Gomez-Vaquero, J. Narvaez, H. Hernando, V.C. Rodriguez, R. Vento, J. Rodriguez Ubreva
    Journal of Translational Medicine, 9(Suppl 2): I3, 2011
Autoimmune disease, Epigenetics; DNA; Epstein Barr-virus
  • ​Blood levels of dual-specificity phosphatase-1independently predict risk for post-operative morbidities causing prolonged hospitilization after coronary artery bypass grafting
    S. Hägg, M. Salehpour, P. Noori, J. Lundström, G. Possnert, R. Takolander, P. Konrad, S. Rosfors, A. Ruusalepp, J. Skogsberg, J. Tegnér, J. Björkegren
    International Journal of Molecular Medicine; 27(6):851-7, 2011
Biological marker; Coronary artery disease; Dual-specificity phosphatase-1; Gene expression profiling
  • ​Carotid plaque age is a feature of plaque stability inversely related to levels of plasma insulin
    S. Hägg, T. Alserius, P. Noori, J. Skogsberg, A. Ruusalepp, T. Ivert, J. Tegnér, and J. Björkegren
    Int. J. Mol. Med. (6):851-7, 2011
Carotid artery diseases; Carotid stenosis; Immunohistochemistry; Insulin; Mass spectrometry
  • Systems medicine and integrated care to combat chronic noncommunicable diseases
    J. Bousquet, J.M. Anto, P.J. Sterk, I.M. Adcock, (…) J. Tegnér, S. Verjovski-Almeida, P. Wellstead, O. Wolkenhauer, E. Wouters, R. Balling, A.J. Brookes, D. Charron, C. Pison, Z. Chen, L. Hood, C. Auffray
    Genome Medicine, 6;3(7):43, 2011
Systems medicine; Cardiovascular disease; Chronic disease; Systems biology
  • ​An atlas of combinatorial transcriptional regulation in mouse and man
    T. Ravasi et al, J. Tegnér was part of the core author team
    Cell. 140(5):744-52, 2010
DNA; Gene expression; Immune system
  • ​DGAT1 participates in the effect of HNF4A on hepatic secretion of triglyceride-rich lipoproteins
    S. Krapivner, M.J. Iglesias, A. Silveira, J. Tegnér, J. Björkegren, A. Hamsten, F.M. van't Hooft
    Arterioscler Thromb Vasc Biol. Feb 18, 962-967, 2010
Gene expression; Lipoproteins; Liver; Metabolism; Transcription
  • ​A vision and strategy for the virtual physiological human in 2010 and beyond
    P. Hunter, P. V. Coveney, B. de Bono, V. Diaz, (…), J. Tegnér, S. Randall Thomas, I. Tollis, I. Tsamardinos, J.H.G.M. van Beek, and M. Viceconti
    Phil. Trans. R. Soc. 368:2595-2614, 2010
Computational physiology; Multi-scale modelling; Physiome; Virtual physiological huma
  • ​Multi-organ expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM domain binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) study
    S. Hägg, J. Skogsberg, J. Lundström, P. Noori, R. Nilsson, H. Zhong, S. Maleki, M.-M. Shang, B. Brinne, M. Bradshaw, V.B. Bajic, (…), J. Tegnér, J. Björkegren
    PLoS Genet, 5(12):e1000754, 2009
Lim domain binding 2; Messenger RNA; Transcription factor; Unclassified drug; Gene expression profiling; Genetic transcription
  • ​The transcriptional network controls growth arrest and differentiation in a human myeloid leukemia cell line
    The FANTOM consortium and RIKEN Genome Exploration Research Group: H. Suzuki et al.
    Nature Genetics, 41(5):553-62, 2009
Base sequence; Cell differentiation; Cell proliferation; Gene expression profiling; Gene regulatory networks; Leukemia, Molecular sequence data
  • ​Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit IET
    O. Eriksson, B. Brinne, Y., Zhou, J. Björkegren, J. Tegnér
    Systems Biology; 3(2):113-29, 2009
Cell cycles; Complex models; Computational biology; Model complexity; Molecular networks; Nonlinear computational models; Protein networks; System analysis
  • ​Bridging the gap between systems biology and medicine
    G. Clermont, C. Auffray, Y. Moreau, D.M. Rocke, D. Dalevi, D. Dubhashi, D.R. Marshall, P. Raasch, F. Dehne, P. Provero, J. Tegnér, B.J. Aronow, M.A. Langston, M. Benson
    Genome Med. Sep 29;1(9):88, 2009
Experimental design; Gene regulatory network; Integrative medicine; Microarray analysis; Priority journal; Protein interaction; Systems biology
  • ​Can modular analysis identify disease-associated candidate genes for therapeutics?
    J. Tegnér
    Journal of Biology, 8:48, 2009
Anti-allergic agents; Automatic data processing; Gene expression profiling; Gene expression regulation; Hypersensitivity
  • ​Computational disease modeling – fact or fiction?
    J. Tegnér, A. Compte, C. Auffray, G. An, G. Cedersund, G. Clermont, B. Gutkin, Z.N. Oltvai, K. Enno Stephan, R. Thomas, P. Villoslada
    Invited meeting report/review, BMC Syst Biol; 3:56, 2009
Biology; Computational Biology; Computer Simulation; Disease; Uncertainty
  • ​Reverse engineering of gene networks with LASSO and non-linear basis functions
    M. Gustafsson, M. Hörnqvist, J. Lundström, J. Björkegren, J. Tegnér
    Annals of the New York Academy of Sciences; 1158:265-75, 2009
DREAM conference; LARS; LASSO; Network inference; Nonlinear; Reverse engineering
  • ​An algorithm for reading dependencies from the minimal undirected independence map of a graphoid that satisfies weak transitivity
    J. Pena, R. Nilsson, J. Björkegren, and J. Tegnér
    The Journal of Machine Learning Research archive, Volume 10, Pages 1071-1094, 2009
Bioinformaties; Graphical models; Graphoids; Vertex separation; Weak transitivity
  • ​Mechanism for Top-down control of working memory capacity
    F. Edin, T. Klingberg, P. Johansson, F. McNab, J. Tegnér, A. Compte
    Proceedings of National Academy of Science, 106(16):6802-7, 2009
Computer model; fMRI; Lateral inhibition; Parietal; Prefrontal; Short-term memory
  • On reliable discovery of molecular signatures
    R. Nilsson, J. Björkegren and J. Tegnér
    BMC Bioinformatics, 10:38, 2009
Accurate prediction; Cancer gene expression; False discovery rate; Molecular signatures; Predictive accuracy; Signature discovery; Statistical framework; Statistical hypothesis testing
  • ​Genome-wide system analysis reveals stable yet flexible network dynamics in yeast
    M. Gustafsson, M. Hörnkvist, J. Björkegren, and J. Tegnér
    IET Systems Biology, Vol 3, p. 219 – 228, 2009
Cell cycle; Cellular network; Design Principles; Dynamical networks; Expression data; Flexible networks; Gene regulatory networks; Higher-order dynamics; Literature mining; Modular coupling
  • Transcription regulatory network analysis using CAGE
    J. Tegnér, J. Björkegren, T. Ravasi, V. Bajic
    Chapter in Cap Analysis Gene Expression (CAGE) The science of decoding gene transcription, Edited by P. Carcinci, Pan Stanford Publishing, 2009
Network analysis; CAGE; Gene expression
  • ​On stability of limit cycles of a prototype problem of piecewise linear systems
    O. Eriksson, J. Tegnér, and Y. Zhou
    Lecture Notes in Control and Information Sciences, Volume 393, Pages 43-55, 2009
Cell membranes; Delta sigma modulation; Linear systems; Machinery; Nonlinear systems; Piecewise linear techniques
  • ​Networks in Biology – from identification, analysis to interpretation
    J. Tegnér
    In I.G. Tollis and M. Patrignani (Eds.): GD 2008, LNCS 5417, p. 1. Springer-Verlag Berlin Heidelberg, 2009
Biological networks; Medical applications
  • ​Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes
    J. Skogsberg, J. Lundström, A. Kovacs, R. Nilsson, P. Noori, S. Maleki, M. Köhler, A. Hamsten, J. Tegnér, J. Björkegren
    PLoS Genetics, 4(3):e1000036, 2008
Apolipoprotein B-100; Atherosclerosis; Carrier proteins; Cholesterol; Foam cells; Gene expression profiling; Macrophages; Mutant strains
  • ​Evidence of highly regulated genes (in-hubs) in gene networks of Saccharomyces cerevisiae
    J. Lundström, J. Björkegren J. Tegnér
    Bioinformatics and Biology Insights,; 2: 313-322, 2008

Gene expression, Network, Algorithm, Transcription regulation, Protein-protein interactions, In-hubs
  • ​ApoB100-LDL Acts as a metabolic signal from liver to peripheral fat causing inhibition of lipolysis in adipocytes
    J. Skogsberg, A. Dicker, M. Rydén, G. Åström, R. Nilsson, A. Mairal, D. Langin, P. Alberts, E. Walum, J. Tegnér, A. Hamsten, P. Arner, and J. Björkegren
    PLoS ONE; 3(11):e3771, 2008
ApoB100-LDL; Cholesterol blood level; Energy metabolism; lipolysis; Metabolic syndrome X
  • ​Electrotonic signals along intracellular membranes may interconnect dendritic spines and nucleus
    I. Sheemer, B. Brinne, J. Tegnér, and S. Grillner
    PLoS Computational Biology Mar 28;4(3):e1000036, 2008
Action potentials; Cell nucleus; Computer simulation; Endoplasmic reticulum; Intracellular membranes; Synaptic transmission
  • ​Integrated approaches to uncovering transcription regulatory networks in mammalian cells
    K. Tan, J. Tegnér, T. Ravasi
    Genomics. 91: 219–231, 2008
Bioinformatics; Cellular networks; ChIp-chip; Dynamics; Gene expression; Gene networks; Genomics; Networks; Personalized medicine; Protein interaction maps; Protein-protein interaction networks; Systems biology
  • ​Detecting multivariate differentially expressed genes
    R. Nilsson, J. M. Peña, J. Björkegren and J. Tegnér
    BMC Bioinformatics 8:150, 2007
Breast neoplasms; Diabetes Mellitus; Gene expression regulation; Multivariate analysis; Protein array analysis
  • ​Human C-reactive protein slows atherosclerosis development in a mouse model with human-like hypercholesterolemia
    A. Kovacs, P. Tornvall, R. Nilsson, J. Tegnér, A. Hamsten, J. Björkegren
    Proceedings of National Academy of Science Aug 21;104(34):13768-73, 2007
Acute-phase protein; Apolipoprotein B100; Coronary artery disease; Low-density lipoprotein; Plaques
  • ​Fronto-parietal connection asymmetry regulates working memory distractibility
    F. Edin, T. Klingberg, J. Stödberg, and J. Tegnér
    Journal of Integrative Neuroscience Issue 4, vol 6, 567-596, 2007
Computational neuroscience; Connectivity; Cortico-cortical interactions; Directed transfer function; Distractibility; EEG; Frontal cortex; Neuronal circuits; Parietal cortex; Working memory
  • ​Perturbations to uncover gene networks
    J. Tegnér, and J. Björkegren
    Trends in Genetics, Jan;23(1):34-41, 2007
Algorithms; Computational biology; Gene regulatory networks; Genomics; Research design; Systems theory
  • Consistent feature selection for pattern recognition in polynomial time
    R. Nilsson, J.M., Peña, J. Björkegren and J. Tegnér
    Journal of Machine Learning Research, 8(March):589-612, 2007
Bioinformatics; Classification; Learning theory; Markov blanket; Relevance
  • ​Neuronal firing rates account for distractor effects on mnemonic accuracy in a visuo-spatial working memory task
    J. Macoveanu, T. Klingberg, and J. Tegnér
    Biological Cybernetics, Vol. 96: 407-419, 2007
Action Potentials; Memory, Nerve net; Space perception; Visual perception
  • ​Stronger synaptic connectivity as a mechanism behind development of working memory-related brain activity during childhood
    F. Edin, J. Macoveanu, P. Olesen, J. Tegnér, and T. Klingberg
    Journal of Cognitive Neuroscience 19:5, 750–760 2007
Adolescent; Brain mapping; Child development; Computer simulation; Magnetic resonance imaging
  • ​Towards scalable and data efficient learning of Markov Boundaries
    J.M. Peña, R. Nilsson, J. Björkegren, and J. Tegnér
    International Journal of Approximate Reasoning, Volume 45, Issue 2, Pages 211-232, 2007
Bayesian networks; Classification; Feature subset selection
  • ​Brain activity related to working memory and distraction in children and adults
    P. Olsen, J. Macoveanu, J. Tegnér, and T. Klingberg
    Cerebral Cortex, May; 17: 1047-1054, 2007
Development; Dorsolateral; Event related; fMRI; Prefrontal; Visuospatial
  • ​Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits
    J. Tegnér, J. Skogsberg, and J. Björkegren
    Journal of Lipid Research, vol 48, 267-277, 2007
Computational modeling; Coronary atherosclerosis; Global gene expression; Individualized medicine; Multicellular disease
  • ​Systembiologin ger möjlighet att förstå komplex sjukdom i detalj
    J. Björkegren and J. Tegnér
    Läkartidningen, 17-23;104(42):3042-5, 2007
Atherosclerosis; Gene expression; Gene regulatory networks; Genetic screening; Genome, Systems Biology
  • ​Learning and validating Bayesian network models of genetic regulatory networks
    J.M. Peña, J. Björkegren, and J. Tegnér
    Advances in Probabilistic Graphical Models, 359-376, Series: Studies in Fuzziness and Soft Computing, Vol. 213. P. Lucas, J. Gámez, A. Salmerón (Eds.) SpringerVerlag, 2007
Bayesian network; Genetic regulatory networks
  • ​Systems biology of innate immunity
    J. Tegnér, R. Nilsson, V.B. Bajic, J. Björkegren, and T. Ravasi
    Cellular Immunology; 244(2):105-9. Epub Apr 11, 2006
Dynamics; Genome; Innate immunity; Macrophages; Networks; Protein-protein interactions; Regulatory circuits; Systems biology; Transcriptional regulation
  • ​A biophysical model of multiple-item working memory: a computational and neuromaging
    J. Macoveanu, T. Klingberg, and J. Tegnér
    Neuroscience, Sep 1;141(3):1611-1618, 2006
Developmental mechanisms; Neural network models; Parietal lobe
  • ​Transcriptional network dynamics in macrophage activation
    R. Nilsson, V.B. Bajic, S. Katayama, H. Suzuki, D. di Bernardo, J. Björkegren, M.J. Sweet, P. Carninci, Y. Hayashizaki, D.A. Hume, J. Tegnér, and T. Ravasi
    Genomics Aug; 88(2):133-142, 2006
Complex systems; Genome; Inflammation; Innate immunity; Macrophages; Network dynamics; Regulatory networks; System biology; Transcriptional regulation
  • ​Detection of compound mode of action by computational integration of whole-genome measurements and genetic perturbations
    K. Hallen, J. Björkegren and J. Tegnér
    BMC Bioinformatics 7:51, 2006
Drug development; Expression measurements; Expression profile; Prior information; Regulatory network; Search strategies; Short interfering RNA (siRNA); Target identification
  • ​A flexible implementation for Support Vector Machines
    R. Nilsson, J. Björkegren, J. Tegnér
    The Mathematica Journal, Vol 10, 1,114-127, 2006
Support Vector Machines (SVM); Pattern recognition; Nonlinear regression
  • ​Evaluating feature selection for SVMs in high dimensions 
    R. Nilsson, J. Pena, J. Björkegren, and J. Tegnér
    Lecture Notes in Computer Science, 719-726, Springer, 2006
Computer simulation; Data reduction; Regularization; Support vector machines (SVM)
  • ​Identifying relevant nodes without learning the model
    J. Pena, R. Nilsson, J. Björkegren, and J. Tegnér
    Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006, Pages 367-374, 2006

Conditional probability distributions; Artificial intelligence; Bayesian networks; Gene expression