An incremental approach to automated protein localisation

<p>Abstract</p> <p>Background</p> <p>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 sequence...

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Main Authors: Kummert Franz, Jensen Nickels, Tscherepanow Marko
Format: Article
Language:English
Published: BMC 2008-10-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/9/445
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spelling doaj-218f056417324455ab8c0aea296aaa9f2020-11-24T23:05:19ZengBMCBMC Bioinformatics1471-21052008-10-019144510.1186/1471-2105-9-445An incremental approach to automated protein localisationKummert FranzJensen NickelsTscherepanow Marko<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> http://www.biomedcentral.com/1471-2105/9/445
collection DOAJ
language English
format Article
sources DOAJ
author Kummert Franz
Jensen Nickels
Tscherepanow Marko
spellingShingle Kummert Franz
Jensen Nickels
Tscherepanow Marko
An incremental approach to automated protein localisation
BMC Bioinformatics
author_facet Kummert Franz
Jensen Nickels
Tscherepanow Marko
author_sort Kummert Franz
title An incremental approach to automated protein localisation
title_short An incremental approach to automated protein localisation
title_full An incremental approach to automated protein localisation
title_fullStr An incremental approach to automated protein localisation
title_full_unstemmed An incremental approach to automated protein localisation
title_sort incremental approach to automated protein localisation
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2008-10-01
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>
url http://www.biomedcentral.com/1471-2105/9/445
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