基于肿瘤基因表达数据的简单有效的基因选择算法_英文_许文龙

第8期
A simple effective g ene selec tion metho d based o n tumo r gene ex pre ssio n data
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interpret these data . Gene selection met hods are proposed to sor t out a small part of g enes that have di sti nctive expressi on sig nat ures across di ff erent sam ples . In t umor research , these discri minat ory genes wil l help t o classify diff erent t umo r ty pes , lead to a bet ter understanding of genetic sig nat ures in cancers and im pro ve t reatment st rat egies . T here are m ainly t w o gene select ion st rategies : fil ter and w rapper m ethods . M any fi lter met hods have been propo sed t hrough elimina ting the redundant g enes based on cri teria [ 2] se t . F or exam ple , Golub et al . provided a sig nalt o-noi se st atistic m ethod f o r binary classif icati on . Cho et al .
0 Introduction
DNA microar ray bi ologi st s to associat e pheno ty pes w it h mo lecule s[ 1] . It i s comm only used f or comparing Received : 2007 -12-07 ; Revi sed : 2008-04 -15
Abstract : A novel gene selection method based o n tumo r g ene ex pression dat a w as proposed . It inco rporat ed the w ithin-class and betw een -class v ariat ions o f t he gene expressio n values to select sig nif icant gene s . It w as evaluated on fo ur publicly available tumo r g ene e xpre ssion dat aset s , using leave-o ne-o ut cro ss-validati on based o n SV M classifier . T he perf ormance w as m easured by t he classi ficat ion accuracies and the top ranked g enes are di scussed and anno tated in special di sease pathw ay s .T he e xperiment al resul ts show that the pro po sed gene select ion method is eff ect ive and robust . T he select ed genes by o ur proposed met ho d in gene ral give mo re accurate classifi catio n result s and t he top ranked g enes are bio logically si gnif icant . Key words : gene selectio n ; fil ter method ; w rapper method ; support vect or m achi ne CLC number : Q786 Document code : A
第 39 卷第 8 期 2009年8月
JOURNAL OF UNIVERSI TY OF SCIENCE AND TECHNOLOGY OF CHINA
V ol . 39 , No . 8 Aug . 200 9
A r ticle ID : 0253 -2778( 2009) 08-0838-05
A simple effective gene selection method based on tumor gene expression data
基于肿瘤基因表达数据的简单有效的基因选择算法
许文龙 , 王立荣 , 张相华 , 冯焕清
( 中国科学技术大学电子科学与技术系 , 安徽合肥 230027)
摘要 : 结合了基因表达数据类内和类间表达差异的信息 , 提出一种新的基因选择算法 , 利用它选择出来的特 征基因表达作为支持向量机的输入特征向量 , 对四个常用数据集进行分类 , 结果表明 , 该方法可以显著提高 分类精度 , 同时通过对选取出来的特征基因在相关信号通路上的分析 , 表明该方法能够得到更多的肿瘤相关 基因 , 具有很强的鲁棒性和很高的精确度 . 关键词 : 基因选择 ; 过滤方法 ; 封装方法 ; 支持向量机 the gene expressio n levels under dif ferent condit ions , such as no rmal o r cancerous tissues . technolo gy has enabled As the e xpression level of t housands o f g enes can be measured simult aneously in a sing le ex periment , a problem arises , t hat i s , ho w t o
[ 6] [ 5]
leukemi a ( AL L) , acut e m yeloid leukemia ( AM L) , and mi xed-lineage leukemia gene ( M L L) , w hich have 24 , 28 , 20 sample s , respectively . Every sample co ntains 12 582 g ene ex pression values . Each sam ple w as normalized to standard distribution-N ( 0 , 1 ) befo re the to p ranked g enes being sco red f or select ion . T he AL L-AM L dat aset[ 10] consist s of 7 129 gene ex pression prof iles of t w o acut e cases o f leukemi a : acute lym phoblasti c leukemia ( A LL , 47 samples)and acute my eloblastic leukemia ( AM L , 25 samples) . T he A LL part of the dat aset co mes f ro m tw o t ypes , B-cell ( A L L-B , 38 samples)and T-cell ( AL L-T , 9 sam ples) , w hile t he AM L pa rt i s split i nt o tw o ty pes , bone m arrow ( AM L-BM , 21 samples) samples and periphe ral blo od ( AM LPB , 4 sam ples)sam ples . Due t o the bipartit ion o f each com po nent , it can be t reat ed bo th as a threeclass datase t ( AL L-B , A L L-T and AM L)and as a four-class dataset ( AL L-B , A L L-T , AM L-BM and AM L-PB ) . H ere , the t hree-class versi on is ref erred t o as A L L-AM L3 and t he fo ur-cl ass version as AL L-AM L-4 . T he A L L dat aset[ 11] co ntains in to tal 248 samples in si x classes of subt ype AL L :T E L , Hy per , T , E2A , M L L , and BC R , w hi ch co nt ai n 79 , 64 , 43 , 27 , 20 and 15 samples , respecti vely . Every sample contains 12 625 gene expression values . A ll t he above f our datase ts have b: Support ed by a G rant f rom In novat ion Cen ter f or Pos tg raduat es at H FN L ( C07-05) . Biography : XU Wen-long , male , born in 1981 , PhD candidat e . Research f ield : bi om edical en gi neering , bioinf ormatics . E -m ail : w lxu @m ail . u st c . edu . cn Corresponding author : FEN G Huan - qi ng , Prof es sor . E -mail : hqf eng @u st c . edu . cn