An incremental approach to automated protein localisation

Tscherepanow M, Jensen N, Kummert F (2008)
BMC Bioinformatics 9(1): 445.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
Volltext vorhanden für diesen Nachweis
Abstract / Bemerkung
Background: The subcellular localisation of proteins in intact living cells is an important means for gaining information about protein functions. Even dynamic processes can be captured, which can barely be predicted based on amino acid sequences. Besides increasing our knowledge about intracellular processes, this information facilitates the development of innovative therapies and new diagnostic methods. In order to perform such a localisation, the proteins under analysis are usually fused with a fluorescent protein. So, they can be observed by means of a fluorescence microscope and analysed. In recent years, several automated methods have been proposed for performing such analyses. Here, two different types of approaches can be distinguished: techniques which enable the recognition of a fixed set of protein locations and methods that identify new ones. To our knowledge, a combination of both approaches – i.e. a technique, which enables supervised learning using a known set of protein locations and is able to identify and incorporate new protein locations afterwards – has not been presented yet. Furthermore, associated problems, e.g. the recognition of cells to be analysed, have usually been neglected. Results: We introduce a novel approach to automated protein localisation in living cells. In contrast to well-known techniques, the protein localisation technique presented in this article aims at combining the two types of approaches described above: After an automatic identification of unknown protein locations, a potential user is enabled to incorporate them into the pre-trained system. An incremental neural network allows the classification of a fixed set of protein location as well as the detection, clustering and incorporation of additional patterns that occur during an experiment. Here, the proposed technique achieves promising results with respect to both tasks. In addition, the protein localisation procedure has been adapted to an existing cell recognition approach. Therefore, it is especially well-suited for high-throughput investigations where user interactions have to be avoided. Conclusion: We have shown that several aspects required for developing an automatic protein localisation technique – namely the recognition of cells, the classification of protein distribution patterns into a set of learnt protein locations, and the detection and learning of new locations – can be combined successfully. So, the proposed method constitutes a crucial step to render image-based protein localisation techniques amenable to large-scale experiments.
BMC Bioinformatics


Tscherepanow M, Jensen N, Kummert F. An incremental approach to automated protein localisation. BMC Bioinformatics. 2008;9(1):445.
Tscherepanow, M., Jensen, N., & Kummert, F. (2008). An incremental approach to automated protein localisation. BMC Bioinformatics, 9(1), 445. doi:10.1186/1471-2105-9-445
Tscherepanow, M., Jensen, N., and Kummert, F. (2008). An incremental approach to automated protein localisation. BMC Bioinformatics 9, 445.
Tscherepanow, M., Jensen, N., & Kummert, F., 2008. An incremental approach to automated protein localisation. BMC Bioinformatics, 9(1), p 445.
M. Tscherepanow, N. Jensen, and F. Kummert, “An incremental approach to automated protein localisation”, BMC Bioinformatics, vol. 9, 2008, pp. 445.
Tscherepanow, M., Jensen, N., Kummert, F.: An incremental approach to automated protein localisation. BMC Bioinformatics. 9, 445 (2008).
Tscherepanow, Marko, Jensen, Nickels, and Kummert, Franz. “An incremental approach to automated protein localisation”. BMC Bioinformatics 9.1 (2008): 445.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Access Level
OA Open Access
Zuletzt Hochgeladen

4 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

In vivo selective imaging and inhibition of leukemia stem-like cells using the fluorescent carbocyanine derivative, DiOC5(3).
Zhang B, Shimada Y, Kuroyanagi J, Ariyoshi M, Nomoto T, Shintou T, Umemoto N, Nishimura Y, Miyazaki T, Tanaka T., Biomaterials 52(), 2015
PMID: 25818410
Counting unstained, confluent cells by modified bright-field microscopy.
Drey LL, Graber MC, Bieschke J., Biotechniques 55(1), 2013
PMID: 23834382

50 References

Daten bereitgestellt von Europe PubMed Central.

Classification of Segmented Regions in Brightfield Microscope Images
Tscherepanow M, Zöllner F, Kummert F., 2006
Recognition of Unstained Live Drosophila Cells in Microscope Images
Tscherepanow M, Jensen N, Kummert F., 2007
Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images
Tscherepanow M, Zöllner F, Hillebrand M, Kummert F., 2008
The establishment of two cell lines from the insect Spodoptera frugiperda (Lepidoptera; Noctuidae).
Vaughn JL, Goodwin RH, Tompkins GJ, McCawley P., In Vitro 13(4), 1977
PMID: 68913
A Manual of Methods for Baculovirus Vectors and Insect Cell Culture Procedures
Summers MD, Smith GE., 1987
Insect Cell Culture Engineering: An Overview
Goosen MFA., 1993
Differences in the expression and localization of human melanotransferrin in lepidopteran and dipteran insect cell lines.
Hegedus DD, Pfeifer TA, Theilmann DA, Kennard ML, Gabathuler R, Jefferies WA, Grigliatti TA., Protein Expr. Purif. 15(3), 1999
PMID: 10092489
An overview of the molecular biology and applications of baculoviruses.
Kuzio J, Faulkner P., Bioprocess Technol 17(), 1993
PMID: 7763505
Comparing N-glycan processing in mammalian cell lines to native and engineered lepidopteran insect cell lines.
Tomiya N, Narang S, Lee YC, Betenbaugh MJ., Glycoconj. J. 21(6), 2004
PMID: 15514482
Physiology of cultured animal cells.
Doverskog M, Ljunggren J, Ohman L, Haggstrom L., J. Biotechnol. 59(1-2), 1997
PMID: 9487719

Madigan MT, Martinko JM, Parker J., 2003
Existence of catalase-less peroxisomes in Sf21 insect cells.
Kurisu M, Morita M, Kashiwayama Y, Yokota S, Hayashi H, Sakai Y, Ohkuma S, Nishimura M, Imanaka T., Biochem. Biophys. Res. Commun. 306(1), 2003
PMID: 12788084
Subcellular localization of the yeast proteome.
Kumar A, Agarwal S, Heyman JA, Matson S, Heidtman M, Piccirillo S, Umansky L, Drawid A, Jansen R, Liu Y, Cheung KH, Miller P, Gerstein M, Roeder GS, Snyder M., Genes Dev. 16(6), 2002
PMID: 11914276
Automated Classification of Subcellular Patterns in Multicell images without Segmentation into Single Cells
Huang K, Murphy RF., 2004
Invariant Image Recognition by Zernike Moments
Khotanzad A, Hong YH., 1990

Soille P., 2003
Texture features for classification of ultrasonic liver images.
Wu CM, Chen YC, Hsieh KS., IEEE Trans Med Imaging 11(2), 1992
PMID: 18218367
Automated image analysis of protein localization in budding yeast.
Chen SC, Zhao T, Gordon GJ, Murphy RF., Bioinformatics 23(13), 2007
PMID: 17646347

A Fast Simplified Fuzzy ARTMAP Network
Vakil-Baghmisheh MT, Pavešić N., 2003
Identifying Fluorescence Microscope Images in Online Journal Articles Using Both Image and Text Features
Hua J, Ayasli ON, Cohen WW, Murphy RF., 2007
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps.
Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB., IEEE Trans Neural Netw 3(5), 1992
PMID: 18276469
Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System
Carpenter GA, Grossberg S, Rosen DB., 1991


Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®


PMID: 18937856
PubMed | Europe PMC

Suchen in

Google Scholar