GraphPart: Homology partitioning for biological sequence analysis

Autor: Felix Teufel, Magnús Halldór Gíslason, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, Ole Winther, Henrik Nielsen

When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too closely related pairs of sequences ending up in different partitions. If this is ignored, performance estimates of prediction m

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::adaf263067d300bea9dd4b95f4e686ee
https://doi.org/10.1101/2023.04.14.536886

NetSolP: predicting protein solubility in E. coli using language models

Autor: Jose Juan Almagro Armenteros, Jesper Salomon, Henrik Nielsen, Alexander Rosenberg Johansen, Vineet Thumuluri, Hannah-Marie Martiny

Publikováno v: Thumuluri, V, Martiny, H-M, Armenteros, J J A, Salomon, J, Nielsen, H, Johansen, A R & Valencia, A (ed.) 2022, ' NetSolP: predicting protein solubility in E. coli using language models ', Bioinformatics, vol. 38, no. 4, pp. 941–946 . https://doi.org/10.1093/bioinformatics/btab801

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a8cc8d7264de88eeb9e888090a2f56b
https://pubmed.ncbi.nlm.nih.gov/34849581

SignalP 6.0 predicts all five types of signal peptides using protein language models

Autor: Felix Teufel, José Juan Almagro Armenteros, Alexander Rosenberg Johansen, Magnús Halldór Gíslason, Silas Irby Pihl, Konstantinos D. Tsirigos, Ole Winther, Søren Brunak, Gunnar von Heijne, Henrik Nielsen

Publikováno v: Teufel, F, Almagro Armenteros, J J, Johansen, A R, Gíslason, M H, Pihl, S I, Tsirigos, K D, Winther, O, Brunak, S, von Heijne, G & Nielsen, H 2022, ' SignalP 6.0 predicts all five types of signal peptides using protein language models ', Nature Biotechnology, vol. 40, pp. 1023-1025 . https://doi.org/10.1038/s41587-021-01156-3
Teufel, F, Almagro Armenteros, J J, Johansen, A R, Gíslason, M H, Pihl, S I, Tsirigos, K D, Winther, O, Brunak, S, von Heijne, G & Nielsen, H 2022, ' SignalP 6.0 predicts all five types of signal peptides using protein language models ', Nature Biotechnology, vol. 40, pp. 1023–1025 . https://doi.org/10.1038/s41587-021-01156-3
Nature Biotechnology, 40 (7)

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa1bee4aab4f399b4e7a5f8798851287
https://pubmed.ncbi.nlm.nih.gov/34980915

Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data

Autor: Morten Lind Jensen, Alexander Rosenberg Johansen, Nicklas Hansen, Jens M. Tarp, Peter Ebert Christensen, Morten Mørup, Ali Mohebbi, Henrik Bengtsson

Publikováno v: EMBC
Mohebbi, A, Johansen, A R, Hansen, N, Christensen, P E, Tarp, J M, Jensen, M L, Bengtsson, H & Morup, M 2020, Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data . in Proceedings of 42 nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020 ., 9176695, IEEE, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, pp. 5140-5145, 42 nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, Quebec, Canada, 20/07/2020 . https://doi.org/10.1109/EMBC44109.2020.9176695

Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9254262a5909840693034f7139cb957f
https://doi.org/10.1109/embc44109.2020.9176695

Prediction of GPI-Anchored proteins with pointer neural networks

Autor: Magnús Halldór Gíslason, Jose Juan Almagro Armenteros, Henrik Nielsen, Alexander Rosenberg Johansen

Publikováno v: Current Research in Biotechnology, Vol 3, Iss, Pp 6-13 (2021)
Gíslason, M H, Nielsen, H, Armenteros, J J A & Johansen, A R 2021, ' Prediction of GPI-anchored proteins with pointer neural networks ', Current Research in Biotechnology, vol. 3, pp. 6-13 . https://doi.org/10.1016/j.crbiot.2021.01.001
Gíslason, M H, Nielsen, H, Almagro Armenteros, J J & Johansen, A R 2021, ' Prediction of GPI-anchored proteins with pointer neural networks ', Current Research in Biotechnology, vol. 3, pp. 6-13 . https://doi.org/10.1016/j.crbiot.2021.01.001

GPI-anchors constitute a very important post-translational modification, linking many proteins to the outer face of the plasma membrane in eukaryotic cells. Since experimental validation of GPI-anchoring signals is slow and costly, computational appr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64eb397b54cadb0578563d4c90a7d37a
https://doi.org/10.1101/838680