Jesper Tegnér mainly investigates Neuroscience, Genetics, Regulation of gene expression, Computational biology and Gene regulatory network. The Working memory, Cortex, Prefrontal cortex and Electrophysiology research Jesper Tegnér does as part of his general Neuroscience study is frequently linked to other disciplines of science, such as Spike, therefore creating a link between diverse domains of science. When carried out as part of a general Genetics research project, his work on DNA methylation, Human genome and Gene is frequently linked to work in Network topology, therefore connecting diverse disciplines of study.
The various areas that Jesper Tegnér examines in his Regulation of gene expression study include Transcription factor, Gene expression, Transcriptional regulation, Cellular differentiation and Coronary artery disease. The concepts of his Transcriptional regulation study are interwoven with issues in DNA binding site, Molecular biology, Cap analysis gene expression, Protein–protein interaction and Cell fate determination. Jesper Tegnér combines subjects such as Regression analysis and Bioinformatics with his study of Gene regulatory network.
Jesper Tegnér mostly deals with Computational biology, Artificial intelligence, Gene regulatory network, Bioinformatics and Systems biology. His research in Computational biology intersects with topics in Genetics, Gene expression, Cellular differentiation, Gene and Regulation of gene expression. His Cellular differentiation research is multidisciplinary, incorporating elements of Self-organizing map, Chromatin and Cell biology.
His studies in Regulation of gene expression integrate themes in fields like Transcription factor and Gene expression profiling. Jesper Tegnér conducts interdisciplinary study in the fields of Gene regulatory network and Reverse engineering through his research. His work carried out in the field of Systems biology brings together such families of science as Personalized medicine and Data science.
His primary areas of study are Computational biology, Artificial intelligence, Multi omics, Cell biology and Cancer research. His Computational biology study combines topics in areas such as RNA-Seq, Proteomics, Chromatin, Messenger RNA and splice. His research on Multi omics also deals with topics like
The Cancer research study combines topics in areas such as Kynurenine pathway, DNA methylation, T cell, Multiple sclerosis and Epigenetics. In his work, Regulation of gene expression is strongly intertwined with Cell cycle, which is a subfield of Cellular differentiation. His research integrates issues of Self-organizing map and Genome in his study of Gene regulatory network.
His primary areas of investigation include Cellular differentiation, Gene regulatory network, Cellular automaton, Data type and Computational biology. The study incorporates disciplines such as Regulation of gene expression, Multi omics and Adaptation in addition to Cellular differentiation. His Regulation of gene expression study integrates concerns from other disciplines, such as Developmental biology, Cell biology, Transcription factor and Housekeeping gene.
He interconnects Complex system, Biological network, Systems biology and Phase space in the investigation of issues within Gene regulatory network. As a part of the same scientific study, Jesper Tegnér usually deals with the Cellular automaton, concentrating on Dynamical systems theory and frequently concerns with Complex network, Theoretical computer science, Algorithmic information theory and Calculus. His study in Computational biology is interdisciplinary in nature, drawing from both RNA-Seq, Proteomics, Metabolomics and Genomics.
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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.
Reverse engineering gene networks using singular value decomposition and robust regression
M. K. S. Yeung;J. Tegner;J. J. Collins.
Proceedings of the National Academy of Sciences of the United States of America (2002)
An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man
Timothy Ravasi;Harukazu Suzuki;Carlo Vittorio Cannistraci;Shintaro Katayama.
Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling
Jesper Tegnér;M. K. Stephen Yeung;Jeff Hasty;James J. Collins.
Proceedings of the National Academy of Sciences of the United States of America (2003)
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.
Nature Genetics (2009)
Data integration in the era of omics: current and future challenges.
David Gomez-Cabrero;Imad Abugessaisa;Dieter Maier;Andrew E. Teschendorff.
BMC Systems Biology (2014)
Mechanism for top-down control of working memory capacity
Fredrik Edin;Torkel Klingberg;Pär Johansson;Fiona McNab.
Proceedings of the National Academy of Sciences of the United States of America (2009)
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.
Proceedings of the National Academy of Sciences of the United States of America (2004)
Intrinsic function of a neuronal network - a vertebrate central pattern generator.
Sten Grillner;Örjan Ekeberg;Abdeljabbar El Manira;Anders Lansner.
Brain Research Reviews (1998)
Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR.
Francesco Marabita;Paola de Candia;Anna Torri;Jesper Tegnér.
Briefings in Bioinformatics (2016)
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