Knut Dagestad Rand

University of Oslo — Norway

Biochemistry, Genetics and Molecular Biology · Life Sciences

10h-index324citations27works2.02yr avg
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Research Topics

Genomics and Phylogenetic Studies(15), RNA and protein synthesis mechanisms(8), Genomics and Chromatin Dynamics(4), Chromosomal and Genetic Variations(4), Genetic Associations and Epidemiology(3)

Publications27 total

StarSignDNA: mutation-to-signature attribution for accurate representation of mutational processes in low-mutation contexts
NAR Genomics and Bioinformatics·2025Open Access
BioNumPy: array programming for biology
Nature Methods·2024· 4 cited
StarSignDNA: Signature tracing for accurate representation of mutational processes
2024· 2 citedOpen Access
KAGE 2: Fast and accurate genotyping of structural variation using pangenomes
2023· 1 citedOpen Access
Ultra-fast genotyping of SNPs and short indels using GPU acceleration
2023Open Access
Connecting Instance and Model Sparsity in Multiple Instance Learning
SSRN Electronic Journal·2023Open Access
simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods
GigaScience·2022· 7 citedOpen Access
BioNumPy: Fast and easy analysis of biological data with Python
2022· 12 citedOpen Access
KAGE: fast alignment-free graph-based genotyping of SNPs and short indels
Genome biology·2022· 22 citedOpen Access
KAGE: Fast alignment-free graph-based genotyping of SNPs and short indels
2021Open Access
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires
Nature Machine Intelligence·2021· 81 citedOpen Access
uio-bmi/immuneML: v2.0.2
Zenodo (CERN European Organization for Nuclear Research)·2021· 1 citedOpen Access
Ten simple rules for quick and dirty scientific programming
PLoS Computational Biology·2021· 22 citedOpen Access
immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires
2021· 17 citedOpen Access
Assessing graph-based read mappers against a baseline approach highlights strengths and weaknesses of current methods
BMC Genomics·2020· 17 citedOpen Access
Beware the Jaccard: the choice of <b>similarity measure</b> is important and non-trivial in genomic colocalisation analysis
Briefings in Bioinformatics·2019· 28 cited
Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes
PLoS Computational Biology·2019· 42 citedOpen Access
Assessing graph-based read mappers against a novel baseline approach highlights strengths and weaknesses of the current generation of methods
2019· 4 citedOpen Access
Beware the Jaccard: the choice of metric is important and non-trivial in genomic colocalisation analysis
2018· 3 citedOpen Access
Graph Peak Caller: calling ChIP-Seq Peaks on Graph-based Reference Genomes
2018· 5 citedOpen Access
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Frequent Co-authors

Geir Kjetil Sandve(23), Ivar Grytten(21), Ingrid K. Glad(10), Alexander J. Nederbragt(9), Milena Pavlović(6), Chakravarthi Kanduri(6), Eivind Hovig(6), Lonneke Scheffer(5), Geir Storvik(5), Lars Holden(5), Sveinung Gundersen(4), Christin Lund‐Andersen(4), Enrico Riccardi(4), Egil Ferkingstad(4), Diana Domańska(4), Sigve Nakken(4), Keshav Motwani(3), Gabriel Balaban(3), Maria Chernigovskaya(3), Ingrid Hobæk Haff(3)