A new vision of evaluating gene expression signatures |
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| Affiliation: | 1. Algorithms and Bioinformatics Research Group, Department of Informatics, King''s College London, Strand, London WC2R 2LS, UK;2. Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri 38039, Turkey;3. School of Science and Technology, Middlesex University, Burroughs, London NW4 4BT, UK;1. Institute of Microbiology, University of Tartu, Ravila 19, Tartu 50411, Estonia;2. Children''s Clinic of Tartu University Hospital, Lunini 6, Tartu 51014, Estonia;3. Department of Family Medicine, University of Tartu, Puusepa 1a, Tartu 50406, Estonia;4. Department of Public Health, University of Tartu, Ravila 19, Tartu 50411, Estonia;1. Division of Interventional Radiology, Department of Radiology, Hospital of the University of Pennsylvania, 1 Silverstein, 3400 Spruce Street, Philadelphia, PA 19104;2. Interventional Radiology, Weill Cornell Imaging at New York-Presbyterian, New York, New York;1. INSIGNEO Institute, University of Sheffield, England, United Kingdom;2. Molecular Gastroenterology Research Group, Department of Oncology and Metabolism, University of Sheffield, England, United Kingdom;3. Department of Computer Science, University of Sheffield, England, United Kingdom;1. Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8562, Japan;2. Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7, Aomi, Koto-ku, Tokyo 135-0064, Japan;3. Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan |
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| Abstract: | Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures. |
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| Keywords: | Bead-chain plot Gene signature evaluation Near-optimal signature Phenotype distinction Swarm intelligence |
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