A Brief History of Protein Sorting Prediction

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A Brief History of Protein Sorting Prediction. / Nielsen, Henrik; Tsirigos, Konstantinos D.; Brunak, Søren; von Heijne, Gunnar.

In: The Protein Journal, Vol. 38, No. 3, 2019, p. 200-216.

Research output: Contribution to journal › Review › Research › peer-review

Harvard

Nielsen, H, Tsirigos, KD, Brunak, S & von Heijne, G 2019, 'A Brief History of Protein Sorting Prediction', The Protein Journal, vol. 38, no. 3, pp. 200-216. https://doi.org/10.1007/s10930-019-09838-3

APA

Nielsen, H., Tsirigos, K. D., Brunak, S., & von Heijne, G. (2019). A Brief History of Protein Sorting Prediction. The Protein Journal, 38(3), 200-216. https://doi.org/10.1007/s10930-019-09838-3

Vancouver

Nielsen H, Tsirigos KD, Brunak S, von Heijne G. A Brief History of Protein Sorting Prediction. The Protein Journal. 2019;38(3):200-216. https://doi.org/10.1007/s10930-019-09838-3

Author

Nielsen, Henrik ; Tsirigos, Konstantinos D. ; Brunak, Søren ; von Heijne, Gunnar. / A Brief History of Protein Sorting Prediction. In: The Protein Journal. 2019 ; Vol. 38, No. 3. pp. 200-216.

Bibtex

@article{98029f3800d9493086f1395da2fefee4,
title = "A Brief History of Protein Sorting Prediction",
abstract = "Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.",
author = "Henrik Nielsen and Tsirigos, {Konstantinos D.} and S{\o}ren Brunak and {von Heijne}, Gunnar",
year = "2019",
doi = "10.1007/s10930-019-09838-3",
language = "English",
volume = "38",
pages = "200--216",
journal = "Protein Journal",
issn = "1572-3887",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - A Brief History of Protein Sorting Prediction

AU - Nielsen, Henrik

AU - Tsirigos, Konstantinos D.

AU - Brunak, Søren

AU - von Heijne, Gunnar

PY - 2019

Y1 - 2019

N2 - Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

AB - Ever since the signal hypothesis was proposed in 1971, the exact nature of signal peptides has been a focus point of research. The prediction of signal peptides and protein subcellular location from amino acid sequences has been an important problem in bioinformatics since the dawn of this research field, involving many statistical and machine learning technologies. In this review, we provide a historical account of how position-weight matrices, artificial neural networks, hidden Markov models, support vector machines and, lately, deep learning techniques have been used in the attempts to predict where proteins go. Because the secretory pathway was the first one to be studied both experimentally and through bioinformatics, our main focus is on the historical development of prediction methods for signal peptides that target proteins for secretion; prediction methods to identify targeting signals for other cellular compartments are treated in less detail.

U2 - 10.1007/s10930-019-09838-3

DO - 10.1007/s10930-019-09838-3

M3 - Review

C2 - 31119599

VL - 38

SP - 200

EP - 216

JO - Protein Journal

JF - Protein Journal

SN - 1572-3887

IS - 3

ER -

ID: 219533634