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  • Multiconstrained gene clustering based on generalized projections

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    66879_1.pdf (475.3Kb)
    Author(s)
    Zeng, Jia
    Zhu, Shanfeng
    Liew, Alan Wee-Chung
    Yan, Hong
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2010
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    Abstract
    Background Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The ...
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    Background Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods. Conclusions The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.
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    Journal Title
    BMC Bioinformatics
    Volume
    11
    Publisher URI
    http://www.biomedcentral.com/content/pdf/1471-2105-11-164.pdf
    Copyright Statement
    © 2010 Liew et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Mathematical sciences
    Biological sciences
    Information and computing sciences
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/38204
    Collection
    • Journal articles

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