Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-7-S4-S23
DC FieldValue
dc.titleSystematic gene function prediction from gene expression data by using a fuzzy nearest-cluster method
dc.contributor.authorLi, X.-L
dc.contributor.authorTan, Y.-C
dc.contributor.authorNg, S.-K
dc.date.accessioned2020-10-20T04:49:15Z
dc.date.available2020-10-20T04:49:15Z
dc.date.issued2006
dc.identifier.citationLi, 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.issn14712105
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/178009
dc.description.abstractBackground: 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.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectExperimental conditions
dc.subjectFunctional annotation
dc.subjectGene Expression Data
dc.subjectGene function prediction
dc.subjectHier-archical clustering
dc.subjectMicroarray experiments
dc.subjectPrediction performance
dc.subjectSimultaneous monitoring
dc.subjectForecasting
dc.subjectGene expression
dc.subjectaccuracy
dc.subjectarticle
dc.subjectcluster analysis
dc.subjectcontrolled study
dc.subjectfungal genetics
dc.subjectfuzzy system
dc.subjectgene expression
dc.subjectgene expression profiling
dc.subjectgene function
dc.subjectgenetic analysis
dc.subjectgenetic heterogeneity
dc.subjectmathematical computing
dc.subjectmicroarray analysis
dc.subjectprediction
dc.subjectquantitative analysis
dc.subjectsupport vector machine
dc.subjectalgorithm
dc.subjectartificial intelligence
dc.subjectbiological model
dc.subjectcluster analysis
dc.subjectcomputer simulation
dc.subjectDNA microarray
dc.subjectevaluation study
dc.subjectfuzzy logic
dc.subjectgenetics
dc.subjectmetabolism
dc.subjectphysiology
dc.subjectprocedures
dc.subjectsignal transduction
dc.subjectproteome
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectCluster Analysis
dc.subjectComputer Simulation
dc.subjectFuzzy Logic
dc.subjectGene Expression
dc.subjectGene Expression Profiling
dc.subjectModels, Biological
dc.subjectOligonucleotide Array Sequence Analysis
dc.subjectProteome
dc.subjectSignal Transduction
dc.typeArticle
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1186/1471-2105-7-S4-S23
dc.description.sourcetitleBMC Bioinformatics
dc.description.volume7
dc.description.issueSUPPL.4
dc.description.pageS23
Appears in Collections:Staff Publications
Elements

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_1471-2105-7-S4-S23.pdf306.66 kBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons