Geir Kjetil Ferkingstad Sandve

Image of Geir Kjetil Ferkingstad Sandve
Norwegian version of this page
Phone +47 22840861
Room 4420
Username
Visiting address Department of Informatics Ole-Johan Dahls hus Gaustadalléen 23B 0373 Oslo
Postal address Postboks 1080 Blindern 0316 Oslo

I am a professor at the Biomedical Informatics Research Group (BMI), Section of Machine learning, Department of informatics (IFI), University of Oslo (UiO). My current research is focused on development of machine learning methodology to learn sequence patterns in immune cells indicative of disease. In particular, I focus on the ability of machine learning models to generalize, on how mechanistic/causal relations underlying a domain can be handled and exploited for machine learning, on development of software platforms for domain-adapted machine learning and on the development of methodology for Multiple-instance learning. I previously worked on statistical genome analysis and motif discovery in DNA. I also have a strong interest for teaching and supervision.

Research group 

We are a bioinformatics group located at the computer science department in Oslo. More information on my team and our ongoing research is provided in the web pages for my research group.

As a computational lab, our only asset is the codebases and accompanying experiences (competencies) we build up through research projects and learning activities. In line with this, our foremost priority is to create a working environment where all members learn, grow and enjoy their work. Our main approach to ensure high quality work is very traditional: working towards papers in leading peer-reviewed journals that will stand the test of time. In addition to this, we try to be deliberate in devising work processes and training activities that provides team members with a comprehensive selection of experiences for a career in bioinformatics or computer science. 

Our approach to research

To do good science, one mainly needs to gain a unique research expertise and find the right research questions to address. This is in reality extremely challenging, touches on several dilemmas, and forms a main motivation for how we work in our group.

How we build a unique research competence:
- Try to be strategic in terms of long-term competence building when selecting research projects and collaborations
- Use team collaboration to allow each member to build a niche competence
- Emphasize reproducibility and reuse of code between projects
- Provide opportunities for both simple scripting and advanced software development

How we position ourselves to find good research questions:
- Work in areas of open and difficult problems
- Collaborate with biomedical groups that bring unique computational challenges

Our approach to training

An education at master level forms a good fundament for doing research, but much more is required to do good science. This is particularly apparent in an interdisciplinary field like bioinformatics. Many aspects are learnt best while doing real research. Other aspects are learnt more effectively through dedicated training. A main priority of our group is to establish infrastructure and work together so as to provide a comprehensive suite of learning opportunities for each member. We aim to provide the infrastructure and guidance to let every team member to get experience with:
- Appropriate programming styles to match the variety of scenarios relevant for computational science, ranging from rapid prototyping of small scripts to development of high quality code for large systems
- Effective software development processes, including use of empowering infrastructure and tools such as using tailored system setups and exploiting capabilities of IDEs.
- Software architecture and software design, as a distinct and often very useful phase prior to coding
- Computational modeling, specifically how to develop mathematically precise problem formulations and corresponding solutions for biomedical problems
- Practices that promote reproducibility of performed analyses and reuse of developed methodology
- Solving algorithmically challenging problems, including considerations of computational efficiency where necessary
- Writing grant applications
- Develop methodology and software in tight collaboration with peers, so as to get exposed to alternative ways of thinking and working at a very detailed level.
- Interact with a relevant international community, so as to ensure that one stays on top of ones field both with respect to science and technological developments

Tags: genome analysis, master tasks, bioinformatics

Publications

  • Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires. BioRxiv. ISSN 0362-4331.
  • Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification . BioRxiv. ISSN 0362-4331.
  • Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). One billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction . BioRxiv. ISSN 0362-4331. doi: 10.1101/2021.07.06.451258.
  • Greiff, Victor & Sandve, Geir Kjetil Ferkingstad (2021). In silico proof of principle of machine learning-based antibody design at unconstrained scale . BioRxiv. ISSN 0362-4331.
  • Akbar, Rahmad; Robert, Philippe Paul Auguste; Pavlović, Milena; Jeliazkov, Jeliazko R.; Snapkov, Igor & Slabodkin, Andrei [Show all 15 contributors for this article] (2021). A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell reports. ISSN 2211-1247. 34:108856(11), p. 1–21. doi: 10.1016/j.celrep.2021.108856. Full text in Research Archive
  • Lemma, Roza Berhanu; Ledsaak, Marit; Fuglerud, Bettina Maria; Sandve, Geir Kjetil; Eskeland, Ragnhild & Gabrielsen, Odd Stokke (2021). Chromatin occupancy and target genes of the haematopoietic master transcription factor MYB. Scientific Reports. ISSN 2045-2322. 11(9008). doi: 10.1038/s41598-021-88516-w.
  • Arnaout, Ramy; Luning Prak, Eline T.; Schwab, Nicholas; Snapkov, Igor; Arora, Rohit & Bashford-Rogers, Rachael [Show all 32 contributors for this article] (2021). The Future of Blood Testing Is the Immunome. Frontiers in Immunology. ISSN 1664-3224. doi: 10.3389/fimmu.2021.626793.
  • Dahal-Koirala, Shiva; Risnes, Louise Fremgaard; Neumann, Ralf Stefan; Christophersen, Asbjørn; Lundin, Knut E. A. & Sandve, Geir Kjetil [Show all 8 contributors for this article] (2021). Comprehensive Analysis of CDR3 Sequences in Gluten-Specific T-Cell Receptors Reveals a Dominant R-Motif and Several New Minor Motifs. Frontiers in Immunology. ISSN 1664-3224. 12:639672, p. 1–13. doi: 10.3389/fimmu.2021.639672. Full text in Research Archive
  • Yao, Ying; Zia, Asima; Neumann, Ralf Stefan; Pavlović, Milena; Balaban, Gabriel & Lundin, Knut Erik Aslaksen [Show all 8 contributors for this article] (2021). T cell receptor repertoire as a potential diagnostic marker for celiac disease. Clinical Immunology. ISSN 1521-6616. 222, p. 1–7. doi: 10.1016/j.clim.2020.108621.
  • Pavlović, Milena; Scheffer, Lonneke; Motwani, Keshav; Kanduri, Chakravarthi; Kompova, Radmila & Vazov, Nikolay Aleksandrov [Show all 41 contributors for this article] (2021). The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nature Machine Intelligence. 3, p. 936–944. doi: 10.1038/s42256-021-00413-z.
  • Khelik, Ksenia; Sandve, Geir Kjetil; Nederbragt, Alexander Johan & Rognes, Torbjørn (2020). NucBreak: location of structural errors in a genome assembly by using paired-end Illumina reads. BMC Bioinformatics. ISSN 1471-2105. 21(1). doi: 10.1186/s12859-020-3414-0. Full text in Research Archive
  • du Pré, Fleur; Blazevski, Jana; Dewan, Alisa Elinsdatter; Stamnæs, Jorunn; Kanduri, Srinivasa Kalyana Chakravarthi & Sandve, Geir Kjetil [Show all 13 contributors for this article] (2020). B cell tolerance and antibody production to the celiac disease autoantigen transglutaminase 2. Journal of Experimental Medicine (JEM). ISSN 0022-1007. 217:e20190860.(2), p. 1–14. doi: 10.1084/jem.20190860. Full text in Research Archive
  • Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan & Sandve, Geir Kjetil (2020). Assessing graph-based read mappers against a baseline approach highlights strengths and weaknesses of current methods. BMC Genomics. ISSN 1471-2164. 21. doi: 10.1186/s12864-020-6685-y. Full text in Research Archive
  • Weber, Cédric R.; Akbar, Rahmad; Yermanos, Alexander; Pavlovic, Milena; Snapkov, Igor & Sandve, Geir Kjetil [Show all 8 contributors for this article] (2020). immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking. Bioinformatics. ISSN 1367-4803. 36(11), p. 3594–3596. doi: 10.1093/bioinformatics/btaa158. Full text in Research Archive
  • Snir, Omri; Kanduri, Srinivasa Kalyana Chakravarthi; Lundin, Knut Erik Aslaksen; Sandve, Geir Kjetil & Sollid, Ludvig Magne (2019). Transcriptional profiling of human intestinal plasma cells reveals effector functions beyond antibody production. United European Gastroenterology journal. ISSN 2050-6406. 0(0), p. 1–9. doi: 10.1177/2050640619862461.
  • Kanduri, Srinivasa Kalyana Chakravarthi; Bock, Christoph; Gundersen, Sveinung; Hovig, Eivind & Sandve, Geir Kjetil (2019). Colocalization analyses of genomic elements: approaches, recommendations and challenges. Bioinformatics. ISSN 1367-4803. 35(9), p. 1615–1624. doi: 10.1093/bioinformatics/bty835. Full text in Research Archive
  • Salvatore, Stefania; Rand, Knut Dagestad; Grytten, Ivar; Ferkingstad, Egil; Domanska, Diana & Holden, Lars [Show all 10 contributors for this article] (2019). Beware the Jaccard: the choice of similarity measure is important and non-trivial in genomic colocalisation analysis. Briefings in Bioinformatics. ISSN 1467-5463. p. 1–8. doi: 10.1093/bib/bbz083.
  • Brown, Alex; Snapkov, Igor; Akbar, Rahmad; Pavlovic, Milena; Miho, Enkelejda & Sandve, Geir Kjetil [Show all 7 contributors for this article] (2019). Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. Molecular Systems Design & Engineering. ISSN 2058-9689. 4(4), p. 701–736. doi: 10.1039/c9me00071b.
  • Gheorghe, Marius; Sandve, Geir Kjetil; Khan, Aziz; Cheneby, Jeanne; Ballester, Benoit & Mathelier, Anthony (2019). A map of direct TF–DNA interactions in the human genome. Nucleic Acids Research (NAR). ISSN 0305-1048. 47(4). doi: 10.1093/nar/gky1210. Full text in Research Archive
  • Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2019). Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Computational Biology. ISSN 1553-734X. 15(2), p. 1–13. doi: 10.1371/journal.pcbi.1006731. Full text in Research Archive
  • Yao, Ying; Zia, Asima; Wyrozemski, Lukasz Adam; Lindeman, Ida; Sandve, Geir Kjetil & Qiao, Shuo Wang (2018). Exploiting antigen receptor information to quantify index switching in single-cell transcriptome sequencing experiments. PLOS ONE. ISSN 1932-6203. 13(12), p. 1–17. doi: 10.1371/journal.pone.0208484. Full text in Research Archive
  • Simovski, Boris; Kanduri, Srinivasa Kalyana Chakravarthi; Gundersen, Sveinung; Titov, Dmytro; Domanska, Diana Ewa & Bock, Christoph [Show all 15 contributors for this article] (2018). Coloc-stats: A unified web interface to perform colocalization analysis of genomic features. Nucleic Acids Research (NAR). ISSN 0305-1048. 46(1), p. W186–W193. doi: 10.1093/nar/gky474. Full text in Research Archive
  • Domanska, Diana Ewa; Kanduri, Srinivasa Kalyana Chakravarthi; Simovski, Boris & Sandve, Geir Kjetil (2018). Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis. BMC Bioinformatics. ISSN 1471-2105. 19(481). doi: 10.1186/s12859-018-2438-1. Full text in Research Archive
  • Risnes, Louise Fremgaard; Christophersen, Asbjørn; Dahal-Koirala, Shiva; Neumann, Ralf Stefan; Sandve, Geir Kjetil & Sarna, Vikas Kumar [Show all 9 contributors for this article] (2018). Disease-driving CD4+ T cell clonotypes persist for decades in celiac disease. Journal of Clinical Investigation. ISSN 0021-9738. 128(6), p. 2642–2650. doi: 10.1172/JCI98819. Full text in Research Archive
  • Rand, Knut Dagestad; Grytten, Ivar; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2017). Coordinates and intervals in graph-based reference genomes. BMC Bioinformatics. ISSN 1471-2105. 18:263, p. 1–8. doi: 10.1186/s12859-017-1678-9. Full text in Research Archive
  • Khelik, Ksenia; Lagesen, Karin; Sandve, Geir Kjetil; Rognes, Torbjørn & Nederbragt, Alexander Johan (2017). NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences. BMC Bioinformatics. ISSN 1471-2105. 18(1), p. 1–14. doi: 10.1186/s12859-017-1748-z. Full text in Research Archive
  • Roy, Bishnudeo; Neumann, Ralf Stefan; Snir, Omri; Iversen, Rasmus; Sandve, Geir Kjetil & Lundin, Knut Erik Aslaksen [Show all 7 contributors for this article] (2017). High-Throughput Single-Cell Analysis of B Cell Receptor Usage among Autoantigen-Specific Plasma Cells in Celiac Disease. Journal of Immunology. ISSN 0022-1767. 199((2)), p. 782–791. doi: 10.4049/jimmunol.1700169. Full text in Research Archive
  • Alsøe, Lene; Sarno, Antonio; Carracedo Huroz, Sergio; Domanska, Diana Ewa; Dingler, Felix & Lirussi, Lisa [Show all 16 contributors for this article] (2017). Uracil Accumulation and Mutagenesis Dominated by Cytosine Deamination in CpG Dinucleotides in Mice Lacking UNG and SMUG1. Scientific Reports. ISSN 2045-2322. 7(1). doi: 10.1038/s41598-017-07314-5. Full text in Research Archive
  • Domanska, Diana Ewa; Vodak, Daniel; Christin, Lund-Andersen; Salvatore, Stefania; Hovig, Eivind & Sandve, Geir Kjetil (2017). The rainfall plot: Its motivation, characteristics and pitfalls. BMC Bioinformatics. ISSN 1471-2105. 18. doi: 10.1186/s12859-017-1679-8.
  • Simovski, Boris; Vodak, Daniel; Gundersen, Sveinung; Domanska, Diana Ewa; Azab, Abdulrahman & Holden, Lars [Show all 25 contributors for this article] (2017). GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience. ISSN 2047-217X. 6(7), p. 1–12. doi: 10.1093/gigascience/gix032. Full text in Research Archive
  • Salvatore, Stefania; Domanska, Diana Ewa; Wood, Mollie; Nordeng, Hedvig Marie Egeland & Sandve, Geir Kjetil (2017). Complex patterns of concomitant medication use: A study among Norwegian women using paracetamol during pregnancy. PLOS ONE. ISSN 1932-6203. 12(12). doi: 10.1371/journal.pone.0190101. Full text in Research Archive
  • Børnich, Claus; Grytten, Ivar; Hovig, Johannes Eivind; Paulsen, Jonas; Cech, Martin & Sandve, Geir Kjetil (2016). Galaxy Portal: Interacting with the galaxy platform through mobile devices. Bioinformatics. ISSN 1367-4803. 32(11), p. 1743–1745. doi: 10.1093/bioinformatics/btw042.
  • Kraus, Hanne Irene; Sandve, Geir Kjetil F.; Schmitz, Martina; Dürst, Matthias & Hovig, Johannes Eivind (2015). Transcriptionally active regions are the preferred targets for chromosomal HPV integration in cervical carcinogenesis. PLOS ONE. ISSN 1932-6203. 10(3). doi: 10.1371/journal.pone.0119566.
  • Ferkingstad, Egil; Holden, Lars & Sandve, Geir Kjetil F. (2015). Monte Carlo Null Models for Genomic Data. Statistical Science. ISSN 0883-4237. 30(1), p. 59–71. doi: 10.1214/14-STS484.
  • Ricigliano, Vito A. G.; Handel, Adam E; Sandve, Geir Kjetil F.; Annibali, Viviana; Ristori, Giovanni & Mechelli, Rosella [Show all 8 contributors for this article] (2015). EBNA2 binds to genomic intervals associated with multiple sclerosis and overlaps with vitamin D receptor occupancy. PLOS ONE. ISSN 1932-6203. 10(4). doi: 10.1371/journal.pone.0119605.
  • Rydbeck, Halfdan; Sandve, Geir Kjetil F.; Ferkingstad, Egil; Simovski, Boris; Rye, Morten Beck & Hovig, Johannes Eivind (2015). ClusTrack: Feature extraction and similarity measures for clustering of genome-wide data sets. PLOS ONE. ISSN 1932-6203. 10(4). doi: 10.1371/journal.pone.0123261. Full text in Research Archive
  • Bengtsen, Mads; Klepper, Kjetil; Gundersen, Sveinung; Cuervo Torre, Ignacio; Drabløs, Finn & Hovig, Johannes Eivind [Show all 9 contributors for this article] (2015). c-Myb Binding Sites in Haematopoietic Chromatin Landscapes. PLOS ONE. ISSN 1932-6203. 10(7). doi: 10.1371/journal.pone.0133280. Full text in Research Archive
  • Mora Ortiz, Antonio Carlos; Sandve, Geir Kjetil; Gabrielsen, Odd Stokke & Eskeland, Ragnhild (2015). In the loop: promoter-enhancer interactions and bioinformatics. Briefings in Bioinformatics. ISSN 1467-5463. doi: 10.1093/bib/bbv097.
  • Disanto, Giulio; Sandve, Geir Kjetil; Ricigliano, Vito AG; Pakpoor, Julia; Berlanga-Taylor, Antonio & Handel, Adam E [Show all 12 contributors for this article] (2014). DNase hypersensitive sites and association with multiple sclerosisxs. Human Molecular Genetics. ISSN 0964-6906. 23(4), p. 942–948. doi: 10.1093/hmg/ddt489.
  • Rye, Morten Beck; Sandve, Geir Kjetil F.; Daub, Carsten O; Kawaji, H; Carninci, P & Forrest, A [Show all 7 contributors for this article] (2014). Chromatin states reveal functional associations for globally defined transcription start sites in four human cell lines. BMC Genomics. ISSN 1471-2164. 15(1). doi: 10.1186/1471-2164-15-120. Full text in Research Archive
  • Molyneux, Sam; Waterhouse, PD; Shelton, Dawne; Shao, Yang W; Watling, Christopher M & Tang, Qing-Lian [Show all 18 contributors for this article] (2014). Human somatic cell mutagenesis creates genetically tractable sarcomas. Nature Genetics. ISSN 1061-4036. 46(9), p. 964–972. doi: 10.1038/ng.3065.
  • Paulsen, Jonas; Sandve, Geir Kjetil F.; Gundersen, Sveinung; Lien, Tonje Gulbrandsen; Trengereid, Kai & Hovig, Johannes Eivind (2014). HiBrowse: Multi-purpose statistical analysis of genome-wide chromatin 3D organization. Bioinformatics. ISSN 1367-4803. 30(11), p. 1620–1622. doi: 10.1093/bioinformatics/btu082. Full text in Research Archive
  • Paulsen, Jonas; Lien, Tonje Gulbrandsen; Sandve, Geir Kjetil; Holden, Lars; Borgan, Ørnulf & Glad, Ingrid Kristine [Show all 7 contributors for this article] (2013). Handling realistic assumptions in hypothesis testing of 3D co-localization of genomic elements. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(10), p. 5164–5174. doi: 10.1093/nar/gkt227.
  • Handel, Adam E; Sandve, Geir Kjetil; Disanto, Giulio; Handunnetthi, Lahiru; Giovannoni, Gavin & Ramagopalan, Sreeram V (2013). Integrating multiple oestrogen receptor alpha ChIP studies: overlap with disease susceptibility regions, DNase I hypersensitivity peaks and gene expression. BMC Medical Genomics. ISSN 1755-8794. 6:45. doi: 10.1186/1755-8794-6-45.
  • Handel, Adam E; Sandve, Geir Kjetil; Disanto, Giulio; Berlanga-Taylor, Antonio; Gallone, Anna & Hanwell, Heather EC [Show all 10 contributors for this article] (2013). Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease. BMC Medicine. ISSN 1741-7015. 11(163). doi: 10.1186/1741-7015-11-163. Full text in Research Archive
  • Sandve, Geir Kjetil; Gundersen, Sveinung; Johansen, Morten; Glad, Ingrid Kristine; Gunathasan, Krishanthi & Holden, Lars [Show all 21 contributors for this article] (2013). The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Research (NAR). ISSN 0305-1048. 41(W1), p. W133–W141. doi: 10.1093/nar/gkt342. Full text in Research Archive
  • Watson, Corey T; Disanto, Giulio; Sandve, Geir Kjetil; Breden, Felix; Giovannoni, Gavin & Ramagopalan, Sreeram V (2012). Age-Associated Hyper-Methylated Regions in the Human Brain Overlap with Bivalent Chromatin Domains. PLOS ONE. ISSN 1932-6203. 7(9). doi: 10.1371/journal.pone.0043840.
  • Bruhn, Sören; Barrenäs, Fredrik; Mobini, R; Andersson, B; Chavali, S. & Egan, B. S. [Show all 12 contributors for this article] (2012). Increased expression of IRF4 and ETS1 in CD4(+) cells from patients with intermittent allergic rhinitis. Allergy. European Journal of Allergy and Clinical Immunology. ISSN 0105-4538. 67(1), p. 33–40. doi: 10.1111/j.1398-9995.2011.02707.x.
  • Disanto, Giulio; Sandve, Geir Kjetil; Berlanga-Taylor, Antonio; Morahan, Julia; Dobson, Ruth & Giovannoni, Gavin [Show all 7 contributors for this article] (2012). Genomic Regions Associated with Multiple Sclerosis Are Active in B Cells. PLOS ONE. ISSN 1932-6203. 7(3). doi: 10.1371/journal.pone.0032281.
  • Disanto, Giulio; Sandve, Geir Kjetil; Berlanga-Taylor, Antonio; Ragnedda, Giammario; Morahan, Julia & Watson, Corey T [Show all 9 contributors for this article] (2012). Vitamin D receptor binding, chromatin states and association with multiple sclerosis. Human Molecular Genetics. ISSN 0964-6906. 21(16), p. 3575–3586. doi: 10.1093/hmg/dds189.
  • Sandve, Geir Kjetil Ferkingstad; Ferkingstad, Egil & Nygård, Ståle (2011). Sequential Monte Carlo multiple testing. Bioinformatics. ISSN 1367-4803. 27(23), p. 3235–3241. doi: 10.1093/bioinformatics/btr568.
  • Gundersen, Sveinung; Kalaš, Matúš; Abul, Osman; Frigessi, Arnoldo; Hovig, Eivind & Sandve, Geir Kjetil (2011). Identifying elemental genomic track types and representing them uniformly. BMC Bioinformatics. ISSN 1471-2105. 12. doi: 10.1186/1471-2105-12-494.
  • Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Show all 15 contributors for this article] (2011). The differential disease regulome. BMC Genomics. ISSN 1471-2164. 12. doi: 10.1186/1471-2164-12-353.
  • Sandve, Geir Kjetil; Gundersen, Sveinung; Rydbeck, Halfdan; Glad, Ingrid Kristine; Holden, Lars & Holden, Marit [Show all 14 contributors for this article] (2010). The Genomic HyperBrowser: inferential genomics at the sequence level. Genome Biology. ISSN 1465-6906. 11(12). doi: 10.1186/gb-2010-11-12-r121. Full text in Research Archive
  • Tøstesen, Eivind; Sandve, Geir Kjetil; Liu, Fang & Hovig, Eivind (2009). Segmentation of DNA sequences into twostate regions and melting fork regions. Journal of Physics: Condensed Matter. ISSN 0953-8984. 21(3). doi: 10.1088/0953-8984/21/3/034109.
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2008). TScan: A two-step de novo motif discovery method. Series on Advances in Bioinformatics and Computational Biology. ISSN 1751-6404. 8.
  • Sandve, Geir Kjetil; Abul, Osman & Drabløs, Finn (2008). Compo: composite motif discovery using discrete models. BMC Bioinformatics. ISSN 1471-2105. 9(527). doi: 10.1186/1471-2105-9-527. Full text in Research Archive
  • Sandve, Geir Kjetil; Abul, Osman & Drabløs, Finn (2008). Compo: composite motif discovery using discrete models. BMC Bioinformatics. ISSN 1471-2105. 9. doi: 10.1186/1471-2105-9-527.
  • Lin, Tien-ho; Ray, Pradipta; Sandve, Geir Kjetil; Uguroglu, Selen & Xing, Eric P. (2008). BayCis: A Bayesian Hierarchical HMM for Cis-Regulatory Module Decoding in Metazoan Genomes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 4955, p. 66–81. doi: 10.1007/978-3-540-78839-3_7.
  • Klepper, Kjetil; Sandve, Geir Kjetil; Abul, Osman; Johansen, Jostein & Drabløs, Finn (2008). Assessment of composite motif discovery methods. BMC Bioinformatics. ISSN 1471-2105. 9(123). doi: 10.1186/1471-2105-9-123. Full text in Research Archive
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2007). False Discovery Rates in Identifying Functional DNA Motifs. In Yang, Jack Y. (Eds.), Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering. IEEE. ISSN 978-1-4244-1509-0. p. 387–394.
  • Sandve, Geir Kjetil; Abul, Osman; Walseng, Vegard & Drabløs, Finn (2007). Improved benchmarks for computational motif discovery. BMC Bioinformatics. ISSN 1471-2105. 8(193). doi: 10.1186/1471-2105-8-193. Full text in Research Archive
  • Sandve, Geir Kjetil; Nedland, Magnar; Bø Syrstad, Øyvind; Eidsheim, Lars Andreas; Abul, Osman & Drabløs, Finn (2006). Accelerating Motif Discovery: Motif Matching on Parallel Hardware. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 4175, p. 197–206.
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2006). A Methodology for Motif Discovery Employing Iterated Cluster Re-assignment. Series on Advances in Bioinformatics and Computational Biology. ISSN 1751-6404. 4, p. 257–268.
  • Sandve, Geir Kjetil & Drabløs, Finn (2006). A survey of motif discovery methods in an integrated framework. Biology Direct. ISSN 1745-6150. 1. doi: 10.1186/1745-6150-1-11.
  • Sandve, Geir Kjetil & Drabløs, Finn (2005). Generalized Composite Motif Discovery. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 3683. doi: 10.1007/11553939.

View all works in Cristin

  • Balaban, Gabriel; Grytten, Ivar; Rand, Knut Dagestad; Scheffer, Lonneke & Sandve, Geir Kjetil (2021). Ten simple rules for quick and dirty scientific programming. PLoS Computational Biology. ISSN 1553-734X. 17:e1008549(3), p. 1–15. doi: 10.1371/journal.pcbi.1008549.
  • Sandve, Geir Kjetil Ferkingstad (2021). ImmuneML and the case for domain-tailored machine learning platforms.
  • Kanduri, Chakravarthi; Domanska, Diana; Hovig, Eivind & Sandve, Geir Kjetil (2017). Genome build information is an essential part of genomic track files. Genome Biology. ISSN 1465-6906. 18(1). doi: 10.1186/s13059-017-1312-1.
  • Alsøe, Lene; Sarno, Antonio; Carracedo Huroz, Sergio; Domanska, Diana Ewa; Dingler, Felix & Lirussi, Lisa [Show all 16 contributors for this article] (2017). Uracil Accumulation and Mutagenesis Dominated by Cytosine Deamination in CpG Dinucleotides in Mice Lacking UNG and SMUG1 .
  • Simovski, Boris; Drabløs, Finn Sverre; Gundersen, Sveinung; Johansen, Morten; Domanska, Diana Ewa & Azab, Abdulrahman [Show all 8 contributors for this article] (2016). The Genomic HyperBrowser.
  • Ferkingstad, Egil; Sandve, Geir Kjetil F. & Holden, Lars (2015). Monte Carlo null models for genomic data.
  • Tørresen, Ole Kristian; Walenz, Brian; Grove, Harald; Lien, Sigbjørn; Knight, James & Star, Bastiaan [Show all 15 contributors for this article] (2014). A new, high quality reference genome assembly for Atlantic cod.
  • Rye, Morten Beck; Sandve, Geir Kjetil F.; Daub, Carsten O; Kawaji, Hideya; Carninci, Piero & Forrest, Alistair [Show all 7 contributors for this article] (2014). Repressed promoters in active chromatin.
  • Rye, Morten Beck; Sandve, Geir Kjetil F.; Daub, Carsten O; Kawaji, Hideya; Carninci, Piero & Forrest, Alistair [Show all 7 contributors for this article] (2014). Chromatin data integrated with a human reference atlas of experimentally defined promoters reveal repressed promoters located in active chromatin.
  • Tørresen, Ole Kristian; Jentoft, Sissel; Star, Bastiaan; Sandve, Geir Kjetil F.; Skage, Morten & Hansen, Marianne Helén Selander [Show all 9 contributors for this article] (2014). A new, high quality reference genome assembly for Atlantic cod.
  • Ferkingstad, Egil; Holden, Lars & Sandve, Geir Kjetil (2013). Monte Carlo null models for genomic data.
  • Sandve, Geir Kjetil; Nekrutenko, Anton; Taylor, James & Hovig, Johannes Eivind (2013). Ten Simple Rules for Reproducible Computational Research. PLoS Computational Biology. ISSN 1553-734X. 9(10). doi: 10.1371/journal.pcbi.1003285.
  • Sandve, Geir Kjetil; Abul, Osman; Walseng, Vegard & Drabløs, Finn (2007). Improved benchmarks for computational motif discovery.
  • Sandve, Geir Kjetil; Stenersen, Kristoffer; Walseng, Vegard; Lekang, Øystein; Klepper, Kjetil & Abul, Osman [Show all 9 contributors for this article] (2006). An integrated approach to motif discovery in DNA sequences.
  • Sandve, Geir Kjetil; Nedland, Magnar; Bø Syrstad, Øyvind; Eidsheim, Lars Andreas; Abul, Osman & Drabløs, Finn (2006). Accelerating motif discovery: motif matching on parallel hardware.
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2006). TScan: A two-step de novo motif discovery method.
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2006). False discovery rates in identifying functional DNA motifs.
  • Abul, Osman; Sandve, Geir Kjetil & Drabløs, Finn (2006). A Methodology for Motif Discovery Employing Iterated Cluster Re-assignment.
  • Sandve, Geir Kjetil (2006). Accelerating Motif Discovery: Motif Matching on Parallel Hardware.
  • Sandve, Geir Kjetil & Drabløs, Finn (2005). Generalized Composite Motif Discovery.
  • Holden, Lars; Jullum, Martin & Sandve, Geir Kjetil (2017). Statistical modeling of repertoire overlap in entire sampling spaces. Norsk Regnesentral.
  • Ferkingstad, Egil; Holden, Lars & Sandve, Geir Kjetil (2013). Monte Carlo null models in ecology. Norsk Regnesentral.
  • Sandve, Geir Kjetil (2008). Potentials and limitations of motif-based binding site prediction in DNA. NTNU-trykk. ISSN 978-82-471-1169-7. 2008(239).

View all works in Cristin

Published Nov. 4, 2010 2:16 PM - Last modified Feb. 24, 2021 10:38 PM