World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
12724
World Ranking
5793
National Ranking
26

Overview

Jesper Tegnér is affiliated with the King Abdullah University of Science and Technology in Saudi Arabia. Their research predominantly lies within the field of Biochemistry, Genetics and Molecular Biology, with a significant focus on Molecular Biology.

Their work spans several subfields of study including Immunology, Artificial Intelligence, Biophysics, and Computational Theory and Mathematics. Key research topics that Jesper Tegnér has contributed to are:

  • Single-cell and spatial transcriptomics
  • Bioinformatics and Genomic Networks
  • Gene expression and cancer classification
  • Immune cells in cancer
  • Cell Image Analysis Techniques
  • Machine Learning in Bioinformatics
  • Gene Regulatory Network Analysis

Jesper Tegnér has authored numerous papers, with some recent notable publications being:

  • "Harmonization of quality metrics and power calculation in multi-omic studies," published in 2020 in Nature Communications
  • "DeepViral: prediction of novel virus-host interactions from protein sequences and infectious disease phenotypes," published in 2021 in Bioinformatics
  • "Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder," published in 2020 in Nature Communications
  • "A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts," published in 2021 in GeroScience
  • "STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline," published in 2021 in Frontiers in Genetics

Frequent collaborators include David Gómez-Cabrero, Narsis A. Kiani, Vincenzo Lagani, Robert Lehmann, and Felipe Prósper. Their publications appear often in venues such as bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), Blood, Nature Machine Intelligence, and the SSRN Electronic Journal.

Jesper Tegnér's academic contributions also extend to book publications, including a work titled Algorithmic Information Dynamics published by Cambridge University Press in 2023.

Best Publications

  • A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data

    Andrew E. Teschendorff;Francesco Marabita;Matthias Lechner;Thomas Bartlett

  • An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man

    Timothy Ravasi;Harukazu Suzuki;Carlo Vittorio Cannistraci;Shintaro Katayama

  • Reverse engineering gene networks using singular value decomposition and robust regression

    M. K. S. Yeung;J. Tegner;J. J. Collins

  • Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling

    Jesper Tegnér;M. K. Stephen Yeung;Jeff Hasty;James J. Collins

  • The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

    Harukazu Suzuki;Alistair R.R. Forrest;Erik Van Nimwegen;Carsten O. Daub

  • Data integration in the era of omics: current and future challenges.

    David Gomez-Cabrero;Imad Abugessaisa;Dieter Maier;Andrew E. Teschendorff

  • Division of labor among distinct subtypes of inhibitory neurons in a cortical microcircuit of working memory

    X.-J. Wang;Jesper Tegnér;Jesper Tegnér;C. Constantinidis;C. Constantinidis;P.S. Goldman-Rakic

  • Mechanism for top-down control of working memory capacity

    Fredrik Edin;Torkel Klingberg;Pär Johansson;Fiona McNab

  • Intrinsic function of a neuronal network - a vertebrate central pattern generator.

    Sten Grillner;Örjan Ekeberg;Abdeljabbar El Manira;Anders Lansner

  • An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training

    Maléne E Lindholm;Francesco Marabita;David Gomez-Cabrero;Helene Rundqvist

  • Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR.

    Francesco Marabita;Paola de Candia;Anna Torri;Jesper Tegnér

  • Towards scalable and data efficient learning of Markov boundaries

    Jose M. Peòa;Roland Nilsson;Johan Björkegren;Jesper Tegnér

  • An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform.

    Francesco Marabita;Malin Almgren;Maléne E. Lindholm;Sabrina Ruhrmann

  • Consistent Feature Selection for Pattern Recognition in Polynomial Time

    Roland Nilsson;José M. Peña;Johan Björkegren;Jesper Tegnér

  • Calcium-dependent potassium channels play a critical role for burst termination in the locomotor network in lamprey.

    A. el Manira;J. Tegner;S. Grillner

  • The dynamical stability of reverberatory neural circuits.

    Jesper Tegnér;Albert Compte;Xiao Jing Wang

  • A vision and strategy for the virtual physiological human in 2010 and beyond.

    Peter Hunter;Peter Hunter;Peter V. Coveney;Bernard de Bono;Vanessa Diaz

  • Spike-timing-dependent plasticity: common themes and divergent vistas.

    Adam Kepecs;Mark C.W. van Rossum;Sen Song;Jesper Tegner

  • Transcriptional network dynamics in macrophage activation.

    Roland Nilsson;Vladimir B. Bajic;Vladimir B. Bajic;Harukazu Suzuki;Diego di Bernardo

  • A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity

    Hector Zenil;Hector Zenil;Santiago Hernández-Orozco;Santiago Hernández-Orozco;Narsis Aftab Kiani;Fernando Soler-Toscano

  • Towards scalable and data efficient learning of Markov boundaries

    Jose M. Pena;Roland Nilsson;Johan Björkegren;Jesper Tegner

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