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Geir Kjetil Sandve
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2020 – today
- 2024
- [j31]Lonneke Scheffer
, Eric Emanuel Reber
Predictability of antigen binding based on short motifs in the antibody CDRH3. Briefings Bioinform. 25(6) (2024) - [j30]Milena Pavlovic
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat. Mac. Intell. 6(1): 15-24 (2024) - [j29]Mai Ha Vu
Linguistics-based formalization of the antibody language as a basis for antibody language models. Nat. Comput. Sci. 4(6): 412-422 (2024) - [c6]Ghadi S. Al Hajj, Aliaksandr Hubin, Chakravarthi Kanduri, Milena Pavlovic, Knut Dagestad Rand, Michael Widrich, Anne H. Schistad Solberg, Victor Greiff, Johan Pensar, Günter Klambauer, Geir Kjetil Sandve:
Incorporating probabilistic domain knowledge into deep multiple instance learning. ICML 2024 - [i8]Ping-Han Hsieh, Ru-Xiu Hsiao, Katalin Ferenc, Anthony Mathelier, Rebekka Burkholz, Chien-Yu Chen, Geir Kjetil Sandve, Tatiana Belova, Marieke L. Kuijjer:
CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data. CoRR abs/2405.18655 (2024) - 2023
- [j28]Ping-Han Hsieh, Camila Miranda Lopes-Ramos, Manuela Zucknick
Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data. Bioinform. 39(10) (2023) - [j27]Mai Ha Vu
Linguistically inspired roadmap for building biologically reliable protein language models. Nat. Mac. Intell. 5(5): 485-496 (2023) - 2022
- [j26]Shiva Dahal-Koirala
TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. Briefings Bioinform. 23(2) (2022) - [j25]Torbjørn Rognes
CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. Bioinform. 38(17): 4230-4232 (2022) - [j24]Geir Kjetil Sandve
Access to ground truth at unconstrained size makes simulated data as indispensable as experimental data for bioinformatics methods development and benchmarking. Bioinform. 38(21): 4994-4996 (2022) - [j23]Philippe A. Robert
Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. Nat. Comput. Sci. 2(12): 845-865 (2022) - [i7]Milena Pavlovic, Ghadi S. Al Hajj, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve:
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. CoRR abs/2204.09291 (2022) - [i6]Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve:
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation. CoRR abs/2205.11234 (2022) - [i5]Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak, Victor Greiff, Geir Kjetil Sandve, Dag Trygve Truslew Haug:
Advancing protein language models with linguistics: a roadmap for improved interpretability. CoRR abs/2207.00982 (2022) - [i4]Mai Ha Vu, Philippe A. Robert, Rahmad Akbar, Bartlomiej Swiatczak, Geir Kjetil Sandve, Dag Trygve Truslew Haug, Victor Greiff:
ImmunoLingo: Linguistics-based formalization of the antibody language. CoRR abs/2209.12635 (2022) - 2021
- [j22]Milena Pavlovic
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 3(11): 936-944 (2021) - [j21]Gabriel Balaban
Ten simple rules for quick and dirty scientific programming. PLoS Comput. Biol. 17(3) (2021) - 2020
- [j20]Stefania Salvatore, Knut Dagestad Rand, Ivar Grytten, Egil Ferkingstad
Beware the Jaccard: the choice of similarity measure is important and non-trivial in genomic colocalisation analysis. Briefings Bioinform. 21(5): 1523-1530 (2020) - [j19]Cédric R. Weber
immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking. Bioinform. 36(11): 3594-3596 (2020) - [j18]Ksenia Khelik, Geir Kjetil Sandve, Alexander Johan Nederbragt, Torbjørn Rognes
NucBreak: location of structural errors in a genome assembly by using paired-end Illumina reads. BMC Bioinform. 21(1): 66 (2020) - [c5]Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer:
Modern Hopfield Networks and Attention for Immune Repertoire Classification. NeurIPS 2020 - [i3]Michael Widrich
Modern Hopfield Networks and Attention for Immune Repertoire Classification. CoRR abs/2007.13505 (2020) - [i2]Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich
Hopfield Networks is All You Need. CoRR abs/2008.02217 (2020)
2010 – 2019
- 2019
- [j17]Chakravarthi Kanduri
Colocalization analyses of genomic elements: approaches, recommendations and challenges. Bioinform. 35(9): 1615-1624 (2019) - [j16]Ivar Grytten
Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Comput. Biol. 15(2) (2019) - 2018
- [j15]Diana Domanska
Mind the gaps: overlooking inaccessible regions confounds statistical testing in genome analysis. BMC Bioinform. 19(1): 481:1-481:9 (2018) - [j14]Boris Simovski, Chakravarthi Kanduri
Coloc-stats: a unified web interface to perform colocalization analysis of genomic features. Nucleic Acids Res. 46(Webserver-Issue): W186-W193 (2018) - 2017
- [j13]Knut D. Rand
Coordinates and intervals in graph-based reference genomes. BMC Bioinform. 18(1): 263:1-263:8 (2017) - [j12]Diana Domanska
The rainfall plot: its motivation, characteristics and pitfalls. BMC Bioinform. 18(1): 264:1-264:11 (2017) - [j11]Ksenia Khelik, Karin Lagesen
NucDiff: in-depth characterization and annotation of differences between two sets of DNA sequences. BMC Bioinform. 18(1): 338 (2017) - 2016
- [j10]Antonio Mora
In the loop: promoter-enhancer interactions and bioinformatics. Briefings Bioinform. 17(6): 980-995 (2016) - [j9]Claus Børnich, Ivar Grytten, Eivind Hovig
Galaxy Portal: interacting with the galaxy platform through mobile devices. Bioinform. 32(11): 1743-1745 (2016) - 2014
- [j8]Jonas Paulsen, Geir Kjetil Sandve
HiBrowse: multi-purpose statistical analysis of genome-wide chromatin 3D organization. Bioinform. 30(11): 1620-1622 (2014) - [i1]Doug James, Nancy Wilkins-Diehr, Victoria Stodden, Dirk Colbry, Carlos Rosales, Mark R. Fahey, Justin Shi, Rafael Ferreira da Silva, Kyo Lee, Ralph Roskies, Laurence Loewe, Susan Lindsey, Rob Kooper, Lorena A. Barba, David H. Bailey, Jonathan M. Borwein, Óscar Corcho, Ewa Deelman, Michael C. Dietze, Benjamin Gilbert, Jan Harkes, Seth Keele, Praveen Kumar, Jong Lee, Erika Linke, Richard Marciano, Luigi Marini, Chris Mattmann, Dave Mattson, Kenton McHenry, Robert T. McLay, Sheila Miguez, Barbara S. Minsker, María S. Pérez-Hernández, Dan Ryan, Mats Rynge, Idafen Santana-Pérez, Mahadev Satyanarayanan, Gloriana St. Clair, Keith Webster, Eivind Hovig, Daniel S. Katz, Sophie Kay, Geir Kjetil Sandve, David Skinner, Gabrielle Allen, John Cazes, Kym Won Cho, Jim Fonseca, Lorraine J. Hwang, Lars Koesterke, Pragnesh Patel, Line Pouchard, Edward Seidel, Isuru Suriarachchi:
Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE. CoRR abs/1412.5557 (2014) - 2013
- [j7]Geir Kjetil Sandve
The Genomic HyperBrowser: an analysis web server for genome-scale data. Nucleic Acids Res. 41(Webserver-Issue): 133-141 (2013) - [j6]Geir Kjetil Sandve
Ten Simple Rules for Reproducible Computational Research. PLoS Comput. Biol. 9(10) (2013) - 2011
- [j5]Geir Kjetil Sandve
Sequential Monte Carlo multiple testing. Bioinform. 27(23): 3235-3241 (2011) - [j4]Sveinung Gundersen
Identifying elemental genomic track types and representing them uniformly. BMC Bioinform. 12: 494 (2011)
2000 – 2009
- 2008
- [b1]Geir Kjetil Ferkingstad Sandve:
Potentials and limitations of motif-based binding site prediction in DNA. Norwegian University of Science and Technology, Trondheim, Norway, 2008 - [j3]Kjetil Klepper, Geir Kjetil Sandve
Assessment of composite motif discovery methods. BMC Bioinform. 9 (2008) - [j2]Geir Kjetil Sandve
Compo: composite motif discovery using discrete models. BMC Bioinform. 9 (2008) - [c4]Tien-ho Lin, Pradipta Ray
BayCis: A Bayesian Hierarchical HMM for Cis-Regulatory Module Decoding in Metazoan Genomes. RECOMB 2008: 66-81 - 2007
- [j1]Geir Kjetil Sandve
Improved benchmarks for computational motif discovery. BMC Bioinform. 8 (2007) - [c3]Osman Abul
False Discovery Rates in Identifying Functional DNA Motifs. BIBE 2007: 387-394 - 2006
- [c2]Geir Kjetil Sandve, Magnar Nedland, Øyvind Bø Syrstad, Lars Andreas Eidsheim, Osman Abul
Accelerating Motif Discovery: Motif Matching on Parallel Hardware. WABI 2006: 197-206 - 2005
- [c1]Geir Kjetil Sandve, Finn Drabløs:
Generalized Composite Motif Discovery. KES (3) 2005: 763-769
Coauthor Index
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last updated on 2024-12-02 21:29 CET by the dblp team
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