Please use this identifier to cite or link to this item:
https://doi.org/10.1186/1471-2105-7-S4-S23
DC Field | Value | |
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dc.title | Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method | |
dc.contributor.author | Li, X.-L | |
dc.contributor.author | Tan, Y.-C | |
dc.contributor.author | Ng, S.-K | |
dc.date.accessioned | 2020-10-20T04:49:15Z | |
dc.date.available | 2020-10-20T04:49:15Z | |
dc.date.issued | 2006 | |
dc.identifier.citation | Li, X.-L, Tan, Y.-C, Ng, S.-K (2006). Systematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method. BMC Bioinformatics 7 (SUPPL.4) : S23. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-7-S4-S23 | |
dc.identifier.issn | 14712105 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/178009 | |
dc.description.abstract | Background: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. Results: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genome-wide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. Conclusion: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches. © 2006 Li et al; licensee BioMed Central Ltd. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | Experimental conditions | |
dc.subject | Functional annotation | |
dc.subject | Gene Expression Data | |
dc.subject | Gene function prediction | |
dc.subject | Hier-archical clustering | |
dc.subject | Microarray experiments | |
dc.subject | Prediction performance | |
dc.subject | Simultaneous monitoring | |
dc.subject | Forecasting | |
dc.subject | Gene expression | |
dc.subject | accuracy | |
dc.subject | article | |
dc.subject | cluster analysis | |
dc.subject | controlled study | |
dc.subject | fungal genetics | |
dc.subject | fuzzy system | |
dc.subject | gene expression | |
dc.subject | gene expression profiling | |
dc.subject | gene function | |
dc.subject | genetic analysis | |
dc.subject | genetic heterogeneity | |
dc.subject | mathematical computing | |
dc.subject | microarray analysis | |
dc.subject | prediction | |
dc.subject | quantitative analysis | |
dc.subject | support vector machine | |
dc.subject | algorithm | |
dc.subject | artificial intelligence | |
dc.subject | biological model | |
dc.subject | cluster analysis | |
dc.subject | computer simulation | |
dc.subject | DNA microarray | |
dc.subject | evaluation study | |
dc.subject | fuzzy logic | |
dc.subject | genetics | |
dc.subject | metabolism | |
dc.subject | physiology | |
dc.subject | procedures | |
dc.subject | signal transduction | |
dc.subject | proteome | |
dc.subject | Algorithms | |
dc.subject | Artificial Intelligence | |
dc.subject | Cluster Analysis | |
dc.subject | Computer Simulation | |
dc.subject | Fuzzy Logic | |
dc.subject | Gene Expression | |
dc.subject | Gene Expression Profiling | |
dc.subject | Models, Biological | |
dc.subject | Oligonucleotide Array Sequence Analysis | |
dc.subject | Proteome | |
dc.subject | Signal Transduction | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1186/1471-2105-7-S4-S23 | |
dc.description.sourcetitle | BMC Bioinformatics | |
dc.description.volume | 7 | |
dc.description.issue | SUPPL.4 | |
dc.description.page | S23 | |
Appears in Collections: | Staff Publications Elements |
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