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Journal article
Linear fuzzy gene network models obtained from microarray data by exhaustive search
BMC bioinformatics, v 5(1), pp 108-108
10 Aug 2004
PMID: 15304201
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
BACKGROUNDRecent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling.RESULTSWe demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes.CONCLUSIONBiological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems.
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Details
- Title
- Linear fuzzy gene network models obtained from microarray data by exhaustive search
- Creators
- Bahrad A Sokhansanj - Drexel UniversityJ Patrick Fitch - Lawrence Livermore National LaboratoryJudy N Quong - Lawrence Livermore National LaboratoryAndrew A Quong - Lawrence Livermore National LaboratoryLawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Publication Details
- BMC bioinformatics, v 5(1), pp 108-108
- Publisher
- Springer BMC
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000223660400001
- Scopus ID
- 2-s2.0-13244298219
- Other Identifier
- 991019168823004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology