简介：MicroRNAs(miRNAs)areendogenouslyexpressednon-codingRNAsof20-24nucleotides,whichpost-transcriptionallyregulategeneexpressioninplantsandanimals.RecentlyithasbeenrecognizedthatmiRNAscompriseoneoftheabundantgenefamiliesinmulticellularspecies,andtheirregulatoryfunctionsinvariousbiologicalprocessesarewidelyspread.Therehasbeenasurgeintheresearchactivitiesinthisfieldinthepastfewyears.Fromtheverybeginning,computationalmethodshavebeenutilizedasindispensabletools,andmanydiscoverieshavebeenobtainedthroughcombinationofexperimentalandcomputationalapproaches.Inthisreview,bothbiologicalandcomputationalaspectsofmiRNAwillbediscussed.AbriefhistoryofthediscoveryofmiRNAanddiscussionofmicroarrayapplicationsinmiRNAresearcharealsoincluded.

简介：Theaimofthisstudyistodesignabiologicalinformationretrievalandanalysissystem(BIRAS)basedontheInternet.Usingthespecificnetworkprotocol,BIRASsystemcouldsendandreceiveinformationfromtheEntrezsearchandretrievalsystemmaintainedbyNationalCenterforBiotechnologyInformation(NCBI)inUSA.Theliteratures,nucleotidesequence,proteinsequences,andotherresourcesaccordingtotheuser-definedtermcouldthenberetrievedandsenttotheuserbypopupmessageorbyE-mailinformingautomaticallyusingBIRASsystem.Alltheinformationretrievingandanalyzingprocessesaredoneinreal-time.Asarobustsystemforintelligentlyanddynamicallyretrievingandanalyzingontheuser-definedinformation,itisbelievedthatBIRASwouldbeextensivelyusedtoretrievespecificinformationfromlargeamountofbiologicaldatabasesinnowdays.Theprogramisavailableonrequestfromthecorrespondingauthor.

简介：构造生物网络是在系统生物学的最重要的问题之一。然而，手工地从数据构造一个网络拿一可观大量时间，因此，一个自动化过程被倡导。自动化网络建设的过程，在这个工作，我们使用二种聪明的计算技术，基因编程和神经计算，推断使用连续变量的二种网络模型。验证介绍途径，实验被进行了，初步的结果证明两条途径能被用来成功地推断网络。

简介：UnderstandingthecouplingspecificitybetweenGprotein-coupledreceptors(GPCRs)andspecificclassesofGproteinsisimportantforfurtherelucidationofreceptorfunctionswithinacell.IncreasinginformationonGPCRsequencesandtheGproteinfamilywouldfacilitatepredictionofthecouplingpropertiesofGPCRs.Inthisstudy,wedescribeanovelapproachforpredictingthecouplingspecificitybetweenGPCRsandGproteins.ThismethodusesnotonlyGPCRsequencesbutalsothefunctionalknowledgegeneratedbynaturallanguagepro-cessing,andcanachieve92.2%predictionaccuracybyusingtheC4.5algorithm.Furthermore,rulesrelatedtoGPCR-Gproteincouplingaregenerated.Thecom-binationofsequenceanalysisandtextminingimprovesthepredictionaccuracyforGPCR-Gproteincouplingspecificity,andalsoprovidescluesforunderstandingGPCRsignaling.