Cancer classification and prediction using logistic regression with Bayesian gene selection

Cancer classification and prediction using logistic regression with Bayesian gene selection

ID：37943443

大?。?48.55 KB

頁數(shù)：11頁

時(shí)間：2019-06-03

Cancer classification and prediction using logistic regression with Bayesian gene selection_第1頁
Cancer classification and prediction using logistic regression with Bayesian gene selection_第2頁
Cancer classification and prediction using logistic regression with Bayesian gene selection_第3頁
Cancer classification and prediction using logistic regression with Bayesian gene selection_第4頁
Cancer classification and prediction using logistic regression with Bayesian gene selection_第5頁
資源描述：

《Cancer classification and prediction using logistic regression with Bayesian gene selection》由會員上傳分享，免費(fèi)在線閱讀，更多相關(guān)內(nèi)容在行業(yè)資料-天天文庫。

1、JournalofBiomedicalInformatics37(2004)249–259www.elsevier.com/locate/yjbinCancerclassi?cationandpredictionusinglogisticregressionwithBayesiangeneselectiona,ba,ba,b,*XiaoboZhou,Kuang-YuLiu,StephenT.C.WongaHarvardCenterforNeurodegenerationandRepair—CenterforBioinformatics,HarvardMedicalSchool,220Longw

2、oodAvenue,Boston,MA02115,USAbRadiologyDepartment,HarvardMedicalSchoolandBrighamandWomen?sHospital,77FrancisStreet,Boston,MA02115,USAReceived13June2004Availableonline11September2004AbstractInmicroarray-basedcancerclassi?cationandprediction,geneselectionisanimportantresearchproblemowingtothelargenum-b

3、erofgenesandthesmallnumberofexperimentalconditions.Inthispaper,weproposeaBayesianapproachtogeneselectionandclassi?cationusingthelogisticregressionmodel.Thebasicideaofourapproachisinconjunctionwithalogisticregressionmodeltorelatethegeneexpressionwiththeclasslabels.WeuseGibbssamplingandMarkovchainMont

4、eCarlo(MCMC)methodstodis-coverimportantgenes.ToimplementGibbsSamplerandMCMCsearch,wederiveaposteriordistributionofselectedgenesgiventheobserveddata.Aftertheimportantgenesareidenti?ed,thesamelogisticregressionmodelisthenusedforcancerclassi?cationandprediction.Issuesfore?cientimplementationforthepropo

5、sedmethodarediscussed.Theproposedmethodisevaluatedagainstseverallargemicroarraydatasets,includinghereditarybreastcancer,smallroundblue-celltumors,andacuteleukemia.Theresultsshowthatthemethodcane?ectivelyidentifyimportantgenesconsistentwiththeknownbiological?ndingswhiletheaccuracyoftheclassi?cationis

6、alsohigh.Finally,therobustnessandsensitivitypropertiesoftheproposedmethodarealsoinvestigated.ó2004ElsevierInc.Allrightsreserved.Keywords:Genemicroarray;Logisticregression;Bayesiangeneselection;Cancerclassi?cation1.Introductionformodelselection[12],andthelogisticregressionmod-el[3].Thelogisticregress

7、ionmodel,alsoknownaslogitCancerclassi?cationandpredictionhasbecomeoneintheliterature,isoneofthemostcommonmodelsforofthemostimportantapplicationsofDNAmicroarrayprediction,regression,andclassi?cationofd

當(dāng)前文檔最多預(yù)覽五頁，下載文檔查看全文

此文檔下載收益歸作者所有

當(dāng)前文檔最多預(yù)覽五頁，下載文檔查看全文
溫馨提示：
1. 部分包含數(shù)學(xué)公式或PPT動畫的文件，查看預(yù)覽時(shí)可能會顯示錯(cuò)亂或異常，文件下載后無此問題，請放心下載。
2. 本文檔由用戶上傳，版權(quán)歸屬用戶，天天文庫負(fù)責(zé)整理代發(fā)布。如果您對本文檔版權(quán)有爭議請及時(shí)聯(lián)系客服。
3. 下載前請仔細(xì)閱讀文檔內(nèi)容，確認(rèn)文檔內(nèi)容符合您的需求后進(jìn)行下載，若出現(xiàn)內(nèi)容與標(biāo)題不符可向本站投訴處理。
4. 下載文檔時(shí)可能由于網(wǎng)絡(luò)波動等原因無法下載或下載錯(cuò)誤，付費(fèi)完成后未能成功下載的用戶請聯(lián)系客服處理。