An incremental approach to automated protein localisation |
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Authors: | Marko Tscherepanow Nickels Jensen Franz Kummert |
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Institution: | 1.Applied Computer Science, Faculty of Technology,Bielefeld University,Bielefeld,Germany;2.Genetics Department, Faculty of Biology,Bielefeld University,Bielefeld,Germany |
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Abstract: | 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. |
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