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Total result(s) found: 20
  • 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