Casper Kaae Sonderby
Overview
Explore the profile of Casper Kaae Sonderby including associated specialties, affiliations and a list of published articles.
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Articles
11
Citations
3251
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Recent Articles
1.
Nissen J, Johansen J, Allesoe R, Sonderby C, Almagro Armenteros J, Gronbech C, et al.
Nat Biotechnol
. 2021 Jan;
39(5):555-560.
PMID: 33398153
Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a program that uses deep variational...
2.
Gronbech C, Vording M, Timshel P, Sonderby C, Pers T, Winther O
Bioinformatics
. 2020 May;
36(16):4415-4422.
PMID: 32415966
Motivation: Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter...
3.
Schantz Klausen M, Jespersen M, Nielsen H, Jensen K, Jurtz V, Sonderby C, et al.
Proteins
. 2019 Feb;
87(6):520-527.
PMID: 30785653
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main...
4.
Almagro Armenteros J, Tsirigos K, Sonderby C, Petersen T, Winther O, Brunak S, et al.
Nat Biotechnol
. 2019 Feb;
37(4):420-423.
PMID: 30778233
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from...
5.
Almagro Armenteros J, Sonderby C, Sonderby S, Nielsen H, Winther O
Bioinformatics
. 2017 Oct;
33(21):3387-3395.
PMID: 29036616
Motivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in...
6.
Almagro Armenteros J, Sonderby C, Sonderby S, Nielsen H, Winther O
Bioinformatics
. 2017 Oct;
33(24):4049.
PMID: 29028934
No abstract available.
7.
Jurtz V, Johansen A, Nielsen M, Almagro Armenteros J, Nielsen H, Sonderby C, et al.
Bioinformatics
. 2017 Sep;
33(22):3685-3690.
PMID: 28961695
Motivation: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational...
8.
Bagger F, Sasivarevic D, Sohi S, Laursen L, Pundhir S, Sonderby C, et al.
Nucleic Acids Res
. 2015 Oct;
44(D1):D917-24.
PMID: 26507857
Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and...
9.
Wu X, Bekker-Jensen I, Christensen J, Rasmussen K, Sidoli S, Qi Y, et al.
Cell Res
. 2015 Oct;
25(11):1205-18.
PMID: 26470845
ASXL1 mutations are frequently found in hematological tumors, and loss of Asxl1 promotes myeloid transformation in mice. Here we present data supporting a role for an ASXL1-BAP1 complex in the...
10.
Lundell H, Sonderby C, Dyrby T
Magn Reson Med
. 2014 Mar;
73(3):1171-6.
PMID: 24639209
Purpose: The short diffusion time regime provides an interesting probe for tissue microstructure and can be investigated with oscillating gradient spin echo (OGSE) experiments. Several studies report new contrasts in...