INTEGRATIVE NETWORK ANALYSIS TO IDENTIFY ABERRANT PATHWAY NETWORKS IN OVARIAN CANCER

Size: px
Start display at page:

Download "INTEGRATIVE NETWORK ANALYSIS TO IDENTIFY ABERRANT PATHWAY NETWORKS IN OVARIAN CANCER"

Transcription

1 INTEGRATIVE NETWORK ANALYSIS TO IDENTIFY ABERRANT PATHWAY NETWORKS IN OVARIAN CANCER LI CHEN 1,2, JIANHUA XUAN 1,*, JINGHUA GU 1, YUE WANG 1, ZHEN ZHANG 2, TIAN LI WANG 2, IE MING SHIH 2 1The Bradley Department of Electrcal and Computer Engneerng, Vrgna Tech 900 N. Glebe Road, Arlngton, VA 22203, USA Emal: {lchen06, xuan, gujh, yuewang}@vt.edu 2Department of Pathology, Johns Hopkns Medcal Insttutons 1550 Orleans Street, Baltmore, MD 21231, USA Emal: {lchen93, zzhang7}@jhm.edu, shhe@yahoo.com, tlw@welch.jhu.edu Ovaran cancer s often called the slent kller snce t s dffcult to have early detecton and prognoss. Understandng the bologcal mechansm related to ovaran cancer becomes extremely mportant for the purpose of treatment. We propose an ntegratve framework to dentfy pathway related networks based on large scale TCGA copy number data and gene expresson profles. The ntegratve approach frst detects hghly conserved copy number altered genes and regards them as seed genes, and then apples a network based method to dentfy subnetworks that can dfferentate gene expresson patterns between dfferent phenotypes of ovaran cancer patents. The dentfed subnetworks are further valdated on an ndependent gene expresson data set usng a network based classfcaton method. The expermental results show that our approach can not only acheve good predcton performance across dfferent data sets, but also dentfy bologcal meanngful subnetworks nvolved n many sgnalng pathways related to ovaran cancer. 1. Introducton Ovaran cancer s the ffth leadng cause of cancer-related deaths among women n the Unted States [1]. It has been estmated that 21,990 women wll be newly dagnosed and 15,460 women wll de of ovaran cancer n 2011 [1]. Most ovaran cancers are serous ovaran carcnomas and only less than 20% of them can be early detected. Prognoss for hgh grade serous carcnoma patents remans unsatsfactory because most patents develop resstance to chemotherapy after surgery and eventually de [2]. Therefore, chemoresstance has been a crtcal clncal problem and t s mportant to understand the bologcal mechansm of ovaran cancer to overcome the resstance to chemotherapy. Many research topcs, such as gene mutaton analyss [3], bomarker dentfcaton [4], etc., have been carred out to study the chemotherapy resstance. Among them, dentfcaton of pathway networks n ovaran cancer becomes an mportant topc n study. * To whom correspondence should be addressed.

2 New technologes have generated large amounts of hgh-throughput genomc and proteomc data related to ovaran cancer, whch make t possble to conduct a comprehensve study from the computatonal pont of vew to better examne the cancer genome. The Cancer Genome Atlas (TCGA) project s the one of the studes that collects varous bologcal data for ovaran cancer usng genome analyss technques. It provdes opportuntes and challenges to develop computatonal methods to study cancers based on multple bologcal data, whch can reveal dfferent aspects and levels of bologcal system functon. Tradtonal computatonal or statstcal approaches, manly focusng on one type of data source, cannot provde a system vew of complex bologcal system. Current and future needs requre sophstcated ntegraton of dverse sets of data, amng to better understand the man features (e.g., components and ther nteractons) of bologcal processes or systems [5, 6]. Many ntegratve approaches have been proposed to study globlastoma based on TCGA data portal [7-9]. A recent study explored mrna expresson, mcrorna expresson, promoter methylaton and DNA copy number data on TCGA ovaran cancer samples and provded bologcally meanngful results successfully [10]. However, the paper [10] has not conducted a sophstcated ntegratve analyss across dfferent data sources. Here, we propose a new ntegratve framework for pathway network dentfcaton n ovaran cancer. Our ntegratve approach s based on the hypothess that the cancer phenotype can be reflected by gene expresson profles, whch are drven by genomc changes at the copy number level. It s also based on the hypothess that the hghly conserved copy number altered genes mght not be dfferentally expressed at the gene expresson level. Therefore, our approach frst detects the consensus regons n the DNA copy number data n hgh-grade ovaran cancer patents and regards the genes wth conserved copy number altered as the seed genes. Then a network dentfcaton method, based on gene expresson profles and proten-proten nteracton network [11], s used to dentfy sgnfcant subnetworks from seed genes that could dfferentate two dfferent phenotypes among patents accordng to the overall survval tme. Fnally, the dentfed subnetworks are cross valdated on a publc gene expresson data set usng a network-based predcton model. Our results show that the proposed ntegratve approach can acheve good predcton performance wth a hgh reproducblty across dfferent data sets. Moreover, t also dentfes several mportant pathway related networks, such as ErbB sgnalng pathway and Notch sgnalng pathway, whch are lkely assocated wth the development of ovaran cancer. 2. Materals and method 2.1. Integratve framework Fg.1 llustrates an ntegratve framework for copy number analyss, subnetwork dentfcaton and predcton by ntegratng DNA copy number data, mrna gene expresson profles, proten-proten nteracton network and clncal nformaton. From DNA copy number data, we detect sgnfcant consensus amplfed and deleted regons usng Genomc Identfcaton of Sgnfcant Targets n

3 fold cross valdaton, AUC = Independent test, AUC = false postve rate Cancer (GISTIC) algorthm [12] and then extract the genes located n these regons. We consder these genes are hghly related to the ovaran cancer mechansm and may functon as drvers to form dfferent phenotypes. We also curate ovaran cancer related genes from lterature [2] and combne them wth the consensus genes from copy number data as seed genes for subnetwork dentfcaton. Then we dentfy subnetworks based on the seed genes usng bootstrappng Markov random feld-based method (BMRF), whch ntegrates proten-proten nteracton networks and the gene expresson profles. For the purpose of evaluaton, we adopt a publc gene expresson data set n the study. We tran a classfer on the TCGA gene expresson data set on the dentfed sgnfcant subnetworks and then test on the publc data set usng network-constraned support vector machnes (netsvm). Fnally we measure the predcton performance and conduct survval analyss on these two gene expresson profles. TCGA DNA copy number data Consensus regons Lterature curated OVC related genes DNA copy number data analyss BMRF subnetwork dentfcaton TCGA mrna gene expresson profles PPI network Seed genes 0 true postve rate Independent test and survval analyss Publc mrna gene expresson profles Sgnfcant Subnetworks Predcton Performance Fg. 1. Illustraton of an ntegratve framework for TCGA ovaran cancer data analyss Data descrpton DNA copy number data and mrna gene expresson data for ovaran serous cystadenocarcnoma cases are obtaned from TCGA data portal. 157 patents have DNA copy number expresson, mrna gene expresson, as well as the clncal survval nformaton. Among them, 58 patents have overall survval tme less than 2 years and 45 patents have overall survval tme larger than 4 years. We categorze these two groups as hgh rsk and low rsk, respectvely. An ndependent publc mrna gene expresson data set (GSE3149) for ovaran cancer cases s obtaned from Bld

4 et al. [13]. Accordngly, 45 patents are grouped n hgh rsk and 49 patents are grouped n low rsk. The copy number data are arrayed by Aglent Human Genome CGH mcroarray 244A chps and both mrna gene expresson profles are arrayed by Affymetrx HG U133A chps. The copy number data are normalzed usng dchp software [14] and mrna gene expresson data are normalzed by Pler and quantle normalzaton methods [15]. Subnetworks are dentfed from proten-proten nteracton (PPI) network obtaned from the HPRD database [16], whch contans about 9,000 genes and 35,000 nteractons. We convert probe set IDs used n gene expresson data to Entrez gene IDs. The probe set ID wth the largest varance across patents samples s used where multple probe set IDs are lnked to one Entrez gene ID. By mappng the PPI network and two data sets we obtan 7,249 genes n 27,885 nteractons to be nvestgated DNA copy number consensus regon detecton The segmentatons on the DNA copy number data are detected by Crcular Bnary Segmentaton (CBS) method [17]. The CBS s a modfed bnary segmentaton method that splts the chromosome nto regons of equal copy number to reduce noses and estmates parameters through permutaton dstrbuton. The sgnfcantly amplfed or deleted genomc regons across the ovaran cancer samples are detected by GISTIC algorthm [12] on the segmented DNA copy number data. The GISTIC algorthm takes segmented copy number data and dentfes regons of the genome that are sgnfcantly amplfed or deleted across a set of samples. A G-score s assgned to each aberraton that consders the ampltude of the aberraton as well as the frequency of ts occurrence across all samples. False Dscovery Rate q-values are then calculated for the aberrant regons, and regons wth q-values below a user-defned threshold are consdered sgnfcant. Here we set both amplfcaton threshold and deleton threshold as 1 (4 copes for amplfcaton and 1 copy for deleton), and the false dscovery rate q-value threshold as Network dentfcaton by bootstrappng MRF (BMRF) We apply a bootstrappng Markov random fle (BMRF) method to the TCGA gene expresson data by ntegratng proten-proten nteracton network to dentfy sgnfcant subnetworks that could dstngush the expresson patterns between hgh rsk and low rsk. BMRF method follows a maxmum a posteror (MAP) prncple to form a novel network score that explctly consders parwse gene nteractons n PPI networks. Let s frst defne a random varable vector f { f,, f } 1 m to represent a set of dscrmnatve scores of m genes (or protens) between two phenotypes. In the context of a PPI network, Let S represent a gene set of m genes n a network and N represent connected neghbors of gene. We defne a parwse clque C 2 on N and S as C2 {, '} ' N, S. The random varable vector f s sad to form a Markov random feld on S wth respect to N and subject to the followng condtons:

5 P() f 0, ff. (1) P( f fs {} ) P( f fn ) The second crteron n Equaton (1) s the Markov property of a random feld, whch states that the probablty of a certan confguraton at gene s statstcally ndependent of the confguratons of all other genes ( j S ) gven confguraton N. The possble confguraton f of a set of random varable vector F obeys a Gbbs dstrbuton f the jont dstrbuton takes the followng form: 1 1 U ( f ) T P() f e, (2) Z 1 U ( ) where Z s a normalzng constant gven by Z e T f and U s gven by U( f) Vc ( f ). ff U s an energy functon that s determned by a sum of clque potentals V c (f) over all clques. Clque potentals allow the modelng of knowledge (a pror) about the contextual nteractons between genes at neghborng stes. For smplcty, we usually assgn 0 potental to all clques of sze greater than 2. The energy U(f) corresponds to the probablty of that confguraton. From Equaton (2), we can see that lower energes correspond to more lkely confguratons. The parameter T s often referred to as temperature that controls the sharpness of the dstrbuton. Z s a normalzaton constant and does not need to be calculated. Denote the observed dscrmnatve scores of genes between two phenotypes as z { z1,, z m }. Here, we defne z as the z-score of ts correspondng p-value p usng 1 1 z (1 p), where s the nverse normal cumulatve densty functon (CDF) [18]. We assume that the observed dscrmnatve score s a result of the addton of ndependent zero mean Gaussan nose to the underlyng dscrmnatve score; z=f+e,e ~ N (0,1). One possble estmate of the underlyng dscrmnatve score f s the MAP estmate ˆf that maxmzes the lkelhood of posteror probablty ( log P( f z ) ); wth the help of Bayes rules and Gbbs dstrbuton, t s equvalent to state that the MAP estmate ˆf mnmzes the followng posteror potental functon: f ˆ arg mn( U ( f ) U ( z f )). The frst term n the posteror potental functon s the pror potental gven by: f 1 f f ' U() f V ( f ) 1 V2 ( f, f' ) f, (3) S S ' N m S k (, ') E d d ' where d s the degree of gene n the PPI network, k s the number of nteractons (or edges), and λ s a trade-off parameter. The frst term n Equaton (3) s the average dscrmnatve score n a subnetwork; the second term n Equaton (3) mposes the smoothness across the subnetwork, whle puttng more weghts on the genes wth large degrees. Note that the posteror potental functon s normalzed by the number of genes and the number of edges n the subnetwork, hence, ndependent of the subnetwork sze. The second term n the posteror potental functon s the lkelhood potental gven by: 2 cc

6 U z f z f, (4) 2 ( ) ( ) /2 m S where γ s a trade-off parameter. The lkelhood potental gves the average square of dfference between observed and underlyng dscrmnatve scores, gven the assumpton of a Gaussan dstrbuton of the nose sgnal wth 0 mean and 1 standard devaton. Thus, we can defne the subnetwork score as the negatve posteror potental functon that takes nto account the dependency among the genes of a subnetwork, whch, n the form of estmated dscrmnatve scores, can be defned as follows: 2 ˆ ˆ ˆ 1 ˆ f f ' ˆ 2 ( ) ( ) ( ) /2 m S k (,') E d m d ' S NetScore G U f Z f z f. (5) Once we defne the MRF-based network score, a modfed smulated annealng search algorthm s then developed to effcently fnd optmal or suboptmal subnetworks wth maxmal network scores. Fnally, to mprove ther reproducblty across data sets, a bootstrappng scheme s mplemented to statstcally select confdent subnetworks. BMRF method has the advantage n dentfyng hub genes that usually express lttle changes among dfferent phenotypes and are hard to detect, therefore mproves the mechansm study of ovaran cancer. In ths experment, we determne the sgnfcant subnetworks accordng to network sze and network score. A network s consdered as sgnfcant f ts sze s greater than 5 and the network score s larger than 1.65 (p 0.05; normal dstrbuton) Network constraned support vector machnes (NetSVM) Gven a tranng sample set (x 1, y 1 ),, (x l, y l ) wth p features and l samples, where y p x R and { 1, 1}, the SVM learnng algorthm ams to fnd a lnear functon of the form p f ( x) β x b, wth β R and b R such that a data pont x s assgned to a label +1 f f(x) > 0, and a label -1 otherwse. Consder a gene network that s represented by a graph G = (V, E, W), where V s a set of vertces that correspond to p genes, E = {u ~ v} s a set of edges ndcatng that gene u and v are lnked on the network and W s the weghts of the edges. The degree of a vertex v s defned as d w( u, v), where w(u, v) ndcates the weght of edge u~v. For ths applcaton, v u the weghts could represent the probabltes of havng edges between two vertces. Followng Chung et al. [19], we defne the Laplacan matrx L of G wth the uv th element to be: 1 wuv (, )/ du f u v and du 0 L ( u, v) w( u, v)/ dudv f u and v are adjacent. (6) 0 otherwse Ths matrx s symmetrc and non-negatve defnte and ts correspondng egenvalues or spectra reflect many propertes of the graph as detaled n [19]. We defne the network-constraned SVM gven non-negatve parameter η as follows:

7 l 1 T T mn βββlβc st.. yβx b1, 0. (7), b, 2 1 Note that L can be wrtten as L = SS T, where S s the matrx whose rows are ndexed by the vertces and whose columns are ndexed by the edges of G such that each column (correspondng to an edge e = {u, v}) has an entry w ( u, v) / du n the row correspondng to u, an entry w ( u, v) / d u n the row correspondng to v, and zero entres elsewhere. Therefore we can see that β T Lβ can be re-wrtten as T u v β Lβ wuv (, ). (8) u~ v du d v From ths representaton we can understand that the added regularzaton term ηβ T Lβ mposes the smoothness of parameters (coeffcents) β over the network va penalzng the weghted sum of squares of the scaled dfference of coeffcents between neghborng vertces n the network. The soluton of Equaton (7) could be obtaned by reducng t to a conventonal SVM optmzaton problem based on the property of L that s symmetrc and sem-postve defnte. We set equal weght 1 for all connectons n PPI database n our experment Classfcaton performance merts and survval analyss For the dentfed subnetworks, we conduct three-fold cross valdaton on TCGA data set and ndependent test on the publc data set. Durng the cross valdaton teraton, each tme we leave one fold as valdaton set and the others as tranng set. Note that the folds are stratfed so that they contan the approxmately same proportons of labels as the orgnal data. The three-fold cross valdaton procedure s repeated 100 tmes n order to get more relable performance estmaton by dfferent randomzatons. The average valdaton performance s reported. We evaluate the predcton performance through several statstcal analyses. Gven the true labels of samples and predcton results, we use the Recever Operatng Characterstc (ROC) curve [20] and the area under the curve (AUC) to measure the predcton accuracy of the classfer. ROC curve s a graphcal plot of true postve rate (TPR) vs. false postve rate (FPR). AUC s an mportant performance measure that provdes an overall measure of accuracy for the predcton. Furthermore, accuracy, senstvty and specfcty are calculated as well. Also, gven the sample survval tme nformaton, we conduct the Kaplan-Meer survval analyss [21] for the predcton results to generate plots for overall survval tme. To compare the dfference of two survval curves, p-value and hazard rato are reported. P-value s calculated by usng the log-rank test and hazard s calculated usng the Cox proportonal model. 3. Results and dscusson Fg. 2 shows the heatmap of the TCGA copy number data and detected consensus regons for both amplfcaton and deleton. There are 869 amplfcaton regons and 979 deleton regons that are 2

8 sgnfcantly consensus across all the hgh grade ovaran cancer tumor samples. 751 and 816 genes are located n the amplfcaton regons and deleton regons, respectvely. Some of these genes are functonally related to knase, transcrpton factor, oncogenes, etc. For example, CCNE1 and MYC are located n the focal amplfcaton regons and they are oncogenes. SMAD4 s located n the focal deleton regon and t s a tumor suppressor gene. These fndngs are bologcally nterestng; however, many of them do not have clear functonal annotaton because of the lmted knowledge. Most of genes are solated n the context of PPI network; and t s dffcult to understand the underlyng bologcal mechansm even though they are mportant to sever as the potental drvers n ovaran cancer. Therefore we treat these genes as the seed genes n the subnetwork dentfcaton methods. We also collect 74 genes assocated wth ovaran cancer pathways from lteratures [2], whch are functonally related to tumor suppressor, oncogenes, sgnalng and tumor bology. Fnally we obtan 511 genes as the seed genes from DNA copy number data after mappng the genes to PPI network. Amplfcaton Deleton (a) (b) Fg. 2. (a) Heatmap of TCGA copy number data and (b) detected sgnfcant consensus regons. We dentfed subnetworks from TCGA gene expresson data and the PPI network, usng BMRF method for all seed genes. Based on the network score and network sze, 36 subnetworks are sgnfcantly (p < 0.05) showng dfferent expresson patterns between hgh rsk and low rsk groups on the TCGA data set. We traned a classfer usng netsvm based on these subnetworks wth cross valdaton. The accuracy (standard devaton) of three-fold cross valdaton s 79.53% (0.0313) wth 76.02% (0.0584) senstvty and 82.26% (0.0312) specfcty. The classfer s further valdated on an ndependent gene expresson data set [13] and acheves 74.47% accuracy wth 62.22% senstvty and 85.71% specfcty. The ROC curves for cross valdaton and ndependent test are shown n Fg. 3. Kaplan-Meer analyss of ndependent test n Fg. 4 also shows sgnfcant dfferent (p = ) n overall survval between two groups predcted as hgh rsk and low rsk. We have also performed predcton usng tradtonal gene selecton method T-

9 test (220 genes wth p < 0.01) and conventonal SVM and acheved 86.92% accuracy for cross valdaton and 69.15% accuracy for ndependent test. As a comparson, our proposed method acheves better reproducblty across dfferent data sets. Moreover the dentfed genes by the proposed method are more bologcally meanngful than the ones selected by T-test n terms of GO functonal annotaton, where many sgnalng pathway related genes are dentfed by the proposed method (see below), whle no sgnfcant pathway s enrched n the genes selected by T- test true postve rate fold cross valdaton, AUC = Independent test, AUC = false postve rate Fg. 3. ROC curves of three-fold cross valdaton on the TCGA data set and ndependent test on the publc data set. Probablty of overall survval Low Rsk Hazard rato = Log-rank p < Hgh Rsk Years Probablty of overall survval Low Rsk Hazard rato = Log-rank p = Hgh Rsk Years (a) (b) Fg. 4. Kaplan-Meer overall survval analyss of three-fold cross valdaton on the (a) TCGA data set and (b) ndependent test on the publc data set. There are n total 224 genes n the dentfed subnetworks, grown from 36 seed genes. Among these seed genes, 29 genes are from copy number altered genes and 11 genes are from lterature collecton (four genes: CCNE1, JAK2, SMAD4 and MYC are overlapped). In terms of gene famly of dentfed seed genes, seven are oncogenes (EGFR, JAK2, JUN, LPP, MYC, NOTCH2 and RAF1); two are tumor suppressors: BRCA1 and SMAD4; and other genes are belongng to transcrpton factors, cell dfferentaton markers, proten knases, etc. Note that CCN1, MYC, BRCA1 are also reported n the study of [10], whch ndcates that our proposed method could dentfy bologcal meanngful genes.

10 We then conducted functonal annotaton and pathway analyss usng MsgDB database [22] for the dentfed subnetworks. The functons of Cell cycle, Apoptoss, and DNA repar are sgnfcantly enrched n some of the subnetworks, shown n Fg. 5. These fndngs are consstent wth our understandng of the cancer development. Extracellular Space Extracellular Space Extracellular Space Downregulated Upregulated (a) (b) (c) Fg. 5. Subnetworks dentfed from TCGA ovaran cancer gene expresson data set. Subnetworks are merged f more than 2 genes are common. Node shape ndcates the seed gene (hexagon) or non-seed gene (ellpse). Node color ndcates the fold change between hgh rsk and low rsk groups. Red represents over-expressed n hgh rsk group and green reflects over-expressed n low rsk group. Enrched pathways and GO functonal annotatons are: (a) ErbB sgnalng pathway: p=2.43e-10; Cell cycle: p=1.47e-07. (b) Notch sgnalng pathway p=1.11e-16; Apoptoss p=3.76e-04. (c) Genes nvolved n Homologous Recombnaton Repar p=8.56e-08; p=6.61e-04. Interestngly, many sgnalng pathways are also sgnfcantly enrched n several subnetworks, for example, ErbB sgnalng pathway (Fg. 5(a), Fg. 6(a)), Notch sgnalng pathway (Fg. 5(b), Fg. 6(b)), NFκB sgnalng pathway (Fg. 7(a)), and TGF beta sgnalng pathway (Fg. 7(b)). Sgnalng pathway s more complcated and dverse. Epdermal growth factor receptor (EGFR) and ERBB2/HER-2 are members of the ErbB famly of tyrosne knase receptors. The studes have shown that the aberrant actvty of EGFR and ERBB2 are mportant n tumor growth and development. Moreover, the overexpresson of EGFR and ERBB2 and ther downstream targets s assocated wth resstance to ovaran cancer chemotherapy [23]. Notch sgnalng pathway has been studed n many papers showng that t s actve n ovaran cancer [24-27]. It s suggested that the nhbton of Notch sgnalng may be a therapeutc strategy for ovaran cancer [27]. NFκB transcrpton factors are key regulators of cell prolferaton and apoptoss [28]. It s beleved that changes n the upstream pathways wll deregulate NFκB actvaton n cancer. Notce that many studes have focused on gene RSF-1 n NFκB network (Fg.7 (a)) and shown that t s nvolved n pacltaxel resstance n ovaran cancer [29, 30]. Transformng growth factor-beta (TGF-beta) s a tumor suppressor, whch s nvolved n many types of human cancer, ncludng ovaran cancer [31]. Recent study also shows that the actvated TGF-b sgnalng pathway n omental metastases of ovaran cancer s a potental therapeutc target [32].

11 Extracellular Space Extracellular Space (a) (b) Fg. 6. Enrched pathways and GO functonal annotatons are: (a) ErbB sgnalng pathway: p=9.10e-13; Sgnal transducton p=2.33e-07. (b) Notch sgnalng pathway p=2.86e-15. Fgure legends are same as the ones n Fg. 5. Extracellular Space Extracellular Space (a) (b) Fg. 7. Enrched pathways and GO functonal annotatons are: (a) NFκB sgnalng pathway p=7.03e-05. (b) TGF beta sgnalng pathway p=1.88e-06. Fgure legends are same as the ones n Fg Concluson We have proposed an ntegratve framework to analyze TCGA ovaran cancer data by ntegratng DNA copy number data, mcroarray data, proten-proten nteracton data and patent clncal nformaton. After detectng hghly conserved copy number altered genes, we have appled network-based methods to dentfy pathway networks that can dfferentate dfferent expresson patterns between dfferent phenotypes. The expermental results have shown that our dentfed subnetworks could acheve good predcton performance and generalzablty on ndependent data set usng a network-constraned classfer. The results also show that our ntegratve approach can dentfy bologcal meanngful subnetworks that related to the development of ovaran cancer and drug resstance.

12 For the future work, the proposed method wll be further enhanced through optmal parameter selecton, statstcal assessment and multple hypothess testng. Moreover, more ovaran cancer samples from TCGA portal and publc data sets wll be nvestgated. 5. Acknowledgments Ths research was supported n part by NIH Grants (CA139246, CA149653, CA149147, and NS A1) and a DoD/CDMRP grant (BC030280). References 1. Jemal, A., F. Bray, et al.,ca Cancer J Cln, 61(2): p ,(2011). 2. Bast, R.C., Jr., B. Hennessy, and G.B. Mlls,Nat Rev Cancer, 9(6): p ,(2009). 3. Norqust, B., K.A. Wurz, et al.,j Cln Oncol,(2011). 4. Tcherkassova, J., C. Abramovch, et al.,tumour Bol,(2011). 5. Hanash, S.,Nat Rev Cancer, 4(8): p ,(2004). 6. Taylor, I.W., R. Lndng, et al.,nat Botechnol, 27(2): p ,(2009). 7. Cooper, L.A., J. Kong, et al.,ieee Trans Bomed Eng, 57(10): p ,(2010). 8. Ovaska, K., M. Laakso, et al.,genome Med, 2(9): p. 65,(2010). 9. Gundem, G., C. Perez-Llamas, et al.,nat Methods, 7(2): p. 92-3,(2010). 10. TCGA,Nature, 474(7353): p ,(2011). 11. Clarke, R., N. Brunner, et al.,proc Natl Acad Sc, 86: p ,(1989). 12. Beroukhm, R., G. Getz, et al.,proc Natl Acad Sc U S A, 104(50): p ,(2007). 13. Bld, A.H., G. Yao, et al.,nature, 439(7074): p ,(2006). 14. Zhao, X., C. L, et al.,cancer Res, 64(9): p ,(2004). 15. Affymetrx,Edted by Affymetrx I. Santa Clara, CA,(2005). 16. Mshra, G.R., M. Suresh, et al.,nuclec Acds Res, 34(Database ssue): p. D411-4,(2006). 17. Olshen, A.B., E.S. Venkatraman, et al.,bostatstcs, 5(4): p ,(2004). 18. Ideker, T., O. Ozer, et al.,bonformatcs, 18 Suppl 1: p. S233-40,(2002). 19. Chung, F., Spectral Graph Theory. CBMS Regnal Conferences Seres. Vol : Amercan Mathematcal Socety, Provdence. 20. Wtten I. and Frank E., Data Mnng: Practcal Machne Learnng Tools and Technques wth Java Implementatons. 2000: Morgan Kaufmann. 21. Kaplan, E. and P. Maer,J AM Stat Assoc, 53: p ,(1958). 22. Subramanan, A., P. Tamayo, et al.,proc Natl Acad Sc U S A, 102(43): p ,(2005). 23. de Graeff, P., A.P. Crjns, et al.,br J Cancer, 99(2): p ,(2008). 24. Rose, S.L.,Int J Gynecol Cancer, 19(4): p ,(2009). 25. Cho, J.H., J.T. Park, et al.,cancer Res, 68(14): p ,(2008). 26. Park, J.T., X. Chen, et al.,am J Pathol, 177(3): p ,(2010). 27. Shh, I.M. and T.L. Wang,Cancer Res, 67(5): p ,(2007). 28. Rayet, B. and C. Gelnas,Oncogene, 18(49): p ,(1999). 29. Sheu, J.J., B. Guan, et al.,j Bol Chem, 285(49): p ,(2010). 30. Cho, J.H., J.J. Sheu, et al.,cancer Res, 69(4): p ,(2009). 31. Zhang, Y.Y., X. L, et al.,y Chuan Xue Bao, 31(8): p ,(2004). 32. Yamamura, S., N. Matsumura, et al.,int J Cancer,(2011).

Copy Number Variation Methods and Data

Copy Number Variation Methods and Data Copy Number Varaton Methods and Data Copy number varaton (CNV) Reference Sequence ACCTGCAATGAT TAAGCCCGGG TTGCAACGTTAGGCA Populaton ACCTGCAATGAT TAAGCCCGGG TTGCAACGTTAGGCA ACCTGCAATGAT TTGCAACGTTAGGCA

More information

Study and Comparison of Various Techniques of Image Edge Detection

Study and Comparison of Various Techniques of Image Edge Detection Gureet Sngh et al Int. Journal of Engneerng Research Applcatons RESEARCH ARTICLE OPEN ACCESS Study Comparson of Varous Technques of Image Edge Detecton Gureet Sngh*, Er. Harnder sngh** *(Department of

More information

IMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE

IMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE IMPROVING THE EFFICIENCY OF BIOMARKER IDENTIFICATION USING BIOLOGICAL KNOWLEDGE JOHN H. PHAN The Wallace H. Coulter Department of Bomedcal Engneerng, Georga Insttute of Technology, 313 Ferst Drve Atlanta,

More information

Statistically Weighted Voting Analysis of Microarrays for Molecular Pattern Selection and Discovery Cancer Genotypes

Statistically Weighted Voting Analysis of Microarrays for Molecular Pattern Selection and Discovery Cancer Genotypes IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.6 No.2, December 26 73 Statstcally Weghted Votng Analyss of Mcroarrays for Molecular Pattern Selecton and Dscovery Cancer Genotypes

More information

AN ENHANCED GAGS BASED MTSVSL LEARNING TECHNIQUE FOR CANCER MOLECULAR PATTERN PREDICTION OF CANCER CLASSIFICATION

AN ENHANCED GAGS BASED MTSVSL LEARNING TECHNIQUE FOR CANCER MOLECULAR PATTERN PREDICTION OF CANCER CLASSIFICATION www.arpapress.com/volumes/vol8issue2/ijrras_8_2_02.pdf AN ENHANCED GAGS BASED MTSVSL LEARNING TECHNIQUE FOR CANCER MOLECULAR PATTERN PREDICTION OF CANCER CLASSIFICATION I. Jule 1 & E. Krubakaran 2 1 Department

More information

Parameter Estimates of a Random Regression Test Day Model for First Three Lactation Somatic Cell Scores

Parameter Estimates of a Random Regression Test Day Model for First Three Lactation Somatic Cell Scores Parameter Estmates of a Random Regresson Test Day Model for Frst Three actaton Somatc Cell Scores Z. u, F. Renhardt and R. Reents Unted Datasystems for Anmal Producton (VIT), Hedeweg 1, D-27280 Verden,

More information

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO Zuo et al. BMC Bonformatcs (2017) 18:99 DOI 10.1186/s12859-017-1515-1 METHODOLOGY ARTICLE Open Access Incorporatng pror bologcal knowledge for network-based dfferental gene expresson analyss usng dfferentally

More information

A MIXTURE OF EXPERTS FOR CATARACT DIAGNOSIS IN HOSPITAL SCREENING DATA

A MIXTURE OF EXPERTS FOR CATARACT DIAGNOSIS IN HOSPITAL SCREENING DATA Journal of Theoretcal and Appled Informaton Technology 2005 ongong JATIT & LLS ISSN: 1992-8645 www.jatt.org E-ISSN: 1817-3195 A MIXTURE OF EXPERTS FOR CATARACT DIAGNOSIS IN HOSPITAL SCREENING DATA 1 SUNGMIN

More information

Reconstruction of gene regulatory network of colon cancer using information theoretic approach

Reconstruction of gene regulatory network of colon cancer using information theoretic approach Reconstructon of gene regulatory network of colon cancer usng nformaton theoretc approach Khald Raza #1, Rafat Parveen * # Department of Computer Scence Jama Mlla Islama (Central Unverst, New Delh-11005,

More information

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS)

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) Internatonal Assocaton of Scentfc Innovaton and Research (IASIR (An Assocaton Unfyng the Scences, Engneerng, and Appled Research Internatonal Journal of Emergng Technologes n Computatonal and Appled Scences

More information

Survival Rate of Patients of Ovarian Cancer: Rough Set Approach

Survival Rate of Patients of Ovarian Cancer: Rough Set Approach Internatonal OEN ACCESS Journal Of Modern Engneerng esearch (IJME) Survval ate of atents of Ovaran Cancer: ough Set Approach Kamn Agrawal 1, ragat Jan 1 Department of Appled Mathematcs, IET, Indore, Inda

More information

Joint Modelling Approaches in diabetes research. Francisco Gude Clinical Epidemiology Unit, Hospital Clínico Universitario de Santiago

Joint Modelling Approaches in diabetes research. Francisco Gude Clinical Epidemiology Unit, Hospital Clínico Universitario de Santiago Jont Modellng Approaches n dabetes research Clncal Epdemology Unt, Hosptal Clínco Unverstaro de Santago Outlne 1 Dabetes 2 Our research 3 Some applcatons Dabetes melltus Is a serous lfe-long health condton

More information

Introduction ORIGINAL RESEARCH

Introduction ORIGINAL RESEARCH ORIGINAL RESEARCH Assessng the Statstcal Sgnfcance of the Acheved Classfcaton Error of Classfers Constructed usng Serum Peptde Profles, and a Prescrpton for Random Samplng Repeated Studes for Massve Hgh-Throughput

More information

Sparse Representation of HCP Grayordinate Data Reveals. Novel Functional Architecture of Cerebral Cortex

Sparse Representation of HCP Grayordinate Data Reveals. Novel Functional Architecture of Cerebral Cortex 1 Sparse Representaton of HCP Grayordnate Data Reveals Novel Functonal Archtecture of Cerebral Cortex X Jang 1, Xang L 1, Jngle Lv 2,1, Tuo Zhang 2,1, Shu Zhang 1, Le Guo 2, Tanmng Lu 1* 1 Cortcal Archtecture

More information

Modeling Multi Layer Feed-forward Neural. Network Model on the Influence of Hypertension. and Diabetes Mellitus on Family History of

Modeling Multi Layer Feed-forward Neural. Network Model on the Influence of Hypertension. and Diabetes Mellitus on Family History of Appled Mathematcal Scences, Vol. 7, 2013, no. 41, 2047-2053 HIKARI Ltd, www.m-hkar.com Modelng Mult Layer Feed-forward Neural Network Model on the Influence of Hypertenson and Dabetes Melltus on Famly

More information

Lymphoma Cancer Classification Using Genetic Programming with SNR Features

Lymphoma Cancer Classification Using Genetic Programming with SNR Features Lymphoma Cancer Classfcaton Usng Genetc Programmng wth SNR Features Jn-Hyuk Hong and Sung-Bae Cho Dept. of Computer Scence, Yonse Unversty, 134 Shnchon-dong, Sudaemoon-ku, Seoul 120-749, Korea hjnh@candy.yonse.ac.kr,

More information

Prediction of Total Pressure Drop in Stenotic Coronary Arteries with Their Geometric Parameters

Prediction of Total Pressure Drop in Stenotic Coronary Arteries with Their Geometric Parameters Tenth Internatonal Conference on Computatonal Flud Dynamcs (ICCFD10), Barcelona, Span, July 9-13, 2018 ICCFD10-227 Predcton of Total Pressure Drop n Stenotc Coronary Arteres wth Ther Geometrc Parameters

More information

Physical Model for the Evolution of the Genetic Code

Physical Model for the Evolution of the Genetic Code Physcal Model for the Evoluton of the Genetc Code Tatsuro Yamashta Osamu Narkyo Department of Physcs, Kyushu Unversty, Fukuoka 8-856, Japan Abstract We propose a physcal model to descrbe the mechansms

More information

Using Past Queries for Resource Selection in Distributed Information Retrieval

Using Past Queries for Resource Selection in Distributed Information Retrieval Purdue Unversty Purdue e-pubs Department of Computer Scence Techncal Reports Department of Computer Scence 2011 Usng Past Queres for Resource Selecton n Dstrbuted Informaton Retreval Sulleyman Cetntas

More information

310 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'16

310 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'16 310 Int'l Conf. Par. and Dst. Proc. Tech. and Appl. PDPTA'16 Akra Sasatan and Hrosh Ish Graduate School of Informaton and Telecommuncaton Engneerng, Toka Unversty, Mnato, Tokyo, Japan Abstract The end-to-end

More information

Incorrect Beliefs. Overconfidence. Types of Overconfidence. Outline. Overprecision 4/22/2015. Econ 1820: Behavioral Economics Mark Dean Spring 2015

Incorrect Beliefs. Overconfidence. Types of Overconfidence. Outline. Overprecision 4/22/2015. Econ 1820: Behavioral Economics Mark Dean Spring 2015 Incorrect Belefs Overconfdence Econ 1820: Behavoral Economcs Mark Dean Sprng 2015 In objectve EU we assumed that everyone agreed on what the probabltes of dfferent events were In subjectve expected utlty

More information

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and Ths artcle appeared n a journal publshed by Elsever. The attached copy s furnshed to the author for nternal non-commercal research and educaton use, ncludng for nstructon at the authors nsttuton and sharng

More information

Journal of Engineering Science and Technology Review 11 (2) (2018) Research Article

Journal of Engineering Science and Technology Review 11 (2) (2018) Research Article Jestr Journal of Engneerng Scence and Technology Revew () (08) 5 - Research Artcle Prognoss Evaluaton of Ovaran Granulosa Cell Tumor Based on Co-forest ntellgence Model Xn Lao Xn Zheng Juan Zou Mn Feng

More information

Prediction of Human Disease-Related Gene Clusters by Clustering Analysis

Prediction of Human Disease-Related Gene Clusters by Clustering Analysis Int. J. Bol. Sc. 2011, 7 61 Research Paper Internatonal Journal of Bologcal Scences 2011; 7(1):61-73 Ivysprng Internatonal Publsher. All rghts reserved Predcton of Human Dsease-Related Gene Clusters by

More information

Optimal Planning of Charging Station for Phased Electric Vehicle *

Optimal Planning of Charging Station for Phased Electric Vehicle * Energy and Power Engneerng, 2013, 5, 1393-1397 do:10.4236/epe.2013.54b264 Publshed Onlne July 2013 (http://www.scrp.org/ournal/epe) Optmal Plannng of Chargng Staton for Phased Electrc Vehcle * Yang Gao,

More information

Insights in Genetics and Genomics

Insights in Genetics and Genomics Insghts n Genetcs and Genomcs Research Artcle Open Access New Score Tests for Equalty of Varances n the Applcaton of DNA Methylaton Data Analyss [Verson ] Welang Qu Xuan L Jarrett Morrow Dawn L DeMeo Scott

More information

Nonstandard Machine Learning Algorithms for Microarray Data Mining. Byoung-Tak Zhang

Nonstandard Machine Learning Algorithms for Microarray Data Mining. Byoung-Tak Zhang Nonstandard Machne Learnng Algorthms for Mcroarray Data Mnng Byoung-Tak Zhang Center for Bonformaton Technology (CBIT) & Bontellgence Laboratory School of Computer Scence and Engneerng Seoul Natonal Unversty

More information

Integrative Computational Identifications of the Signaling Pathway Network Related to TNF-alpha Stimulus in Vascular Endothelial Cells

Integrative Computational Identifications of the Signaling Pathway Network Related to TNF-alpha Stimulus in Vascular Endothelial Cells Integratve Computatonal Identfcatons of the Sgnalng Pathway Network Related to -alpha Stmulus n Vascular Endothelal Cells Jn Gu, Shao L, Yang Chen, Yanda L MOE Key Laboratory of Bonformatcs and Bonformatcs

More information

AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THRESHOLDING AND SVM

AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THRESHOLDING AND SVM AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THRESHOLDING AND SVM Wewe Gao 1 and Jng Zuo 2 1 College of Mechancal Engneerng, Shangha Unversty of Engneerng Scence, Shangha,

More information

Fast Algorithm for Vectorcardiogram and Interbeat Intervals Analysis: Application for Premature Ventricular Contractions Classification

Fast Algorithm for Vectorcardiogram and Interbeat Intervals Analysis: Application for Premature Ventricular Contractions Classification Fast Algorthm for Vectorcardogram and Interbeat Intervals Analyss: Applcaton for Premature Ventrcular Contractons Classfcaton Irena Jekova, Vessela Krasteva Centre of Bomedcal Engneerng Prof. Ivan Daskalov

More information

Lateral Transfer Data Report. Principal Investigator: Andrea Baptiste, MA, OT, CIE Co-Investigator: Kay Steadman, MA, OTR, CHSP. Executive Summary:

Lateral Transfer Data Report. Principal Investigator: Andrea Baptiste, MA, OT, CIE Co-Investigator: Kay Steadman, MA, OTR, CHSP. Executive Summary: Samar tmed c ali ndus t r esi nc 55Fl em ngdr ve, Un t#9 Cambr dge, ON. N1T2A9 T el. 18886582206 Ema l. nf o@s amar t r ol l boar d. c om www. s amar t r ol l boar d. c om Lateral Transfer Data Report

More information

INITIAL ANALYSIS OF AWS-OBSERVED TEMPERATURE

INITIAL ANALYSIS OF AWS-OBSERVED TEMPERATURE INITIAL ANALYSIS OF AWS-OBSERVED TEMPERATURE Wang Yng, Lu Xaonng, Ren Zhhua, Natonal Meteorologcal Informaton Center, Bejng, Chna Tel.:+86 684755, E-mal:cdcsjk@cma.gov.cn Abstract From, n Chna meteorologcal

More information

Gene Selection Based on Mutual Information for the Classification of Multi-class Cancer

Gene Selection Based on Mutual Information for the Classification of Multi-class Cancer Gene Selecton Based on Mutual Informaton for the Classfcaton of Mult-class Cancer Sheng-Bo Guo,, Mchael R. Lyu 3, and Tat-Mng Lok 4 Department of Automaton, Unversty of Scence and Technology of Chna, Hefe,

More information

Integration of sensory information within touch and across modalities

Integration of sensory information within touch and across modalities Integraton of sensory nformaton wthn touch and across modaltes Marc O. Ernst, Jean-Perre Brescan, Knut Drewng & Henrch H. Bülthoff Max Planck Insttute for Bologcal Cybernetcs 72076 Tübngen, Germany marc.ernst@tuebngen.mpg.de

More information

BINNING SOMATIC MUTATIONS BASED ON BIOLOGICAL KNOWLEDGE FOR PREDICTING SURVIVAL: AN APPLICATION IN RENAL CELL CARCINOMA

BINNING SOMATIC MUTATIONS BASED ON BIOLOGICAL KNOWLEDGE FOR PREDICTING SURVIVAL: AN APPLICATION IN RENAL CELL CARCINOMA BINNING SOMATIC MUTATIONS BASED ON BIOLOGICAL KNOWLEDGE FOR PREDICTING SURVIVAL: AN APPLICATION IN RENAL CELL CARCINOMA DOKYOON KIM, RUOWANG LI, SCOTT M. DUDEK, JOHN R. WALLACE, MARYLYN D. RITCHIE Center

More information

A New Machine Learning Algorithm for Breast and Pectoral Muscle Segmentation

A New Machine Learning Algorithm for Breast and Pectoral Muscle Segmentation Avalable onlne www.ejaet.com European Journal of Advances n Engneerng and Technology, 2015, 2(1): 21-29 Research Artcle ISSN: 2394-658X A New Machne Learnng Algorthm for Breast and Pectoral Muscle Segmentaton

More information

Biomarker Selection from Gene Expression Data for Tumour Categorization Using Bat Algorithm

Biomarker Selection from Gene Expression Data for Tumour Categorization Using Bat Algorithm Receved: March 20, 2017 401 Bomarker Selecton from Gene Expresson Data for Tumour Categorzaton Usng Bat Algorthm Gunavath Chellamuthu 1 *, Premalatha Kandasamy 2, Svasubramanan Kanagaraj 3 1 School of

More information

Balanced Query Methods for Improving OCR-Based Retrieval

Balanced Query Methods for Improving OCR-Based Retrieval Balanced Query Methods for Improvng OCR-Based Retreval Kareem Darwsh Electrcal and Computer Engneerng Dept. Unversty of Maryland, College Park College Park, MD 20742 kareem@glue.umd.edu Douglas W. Oard

More information

Evaluation of the generalized gamma as a tool for treatment planning optimization

Evaluation of the generalized gamma as a tool for treatment planning optimization Internatonal Journal of Cancer Therapy and Oncology www.jcto.org Evaluaton of the generalzed gamma as a tool for treatment plannng optmzaton Emmanoul I Petrou 1,, Ganesh Narayanasamy 3, Eleftheros Lavdas

More information

Modeling the Survival of Retrospective Clinical Data from Prostate Cancer Patients in Komfo Anokye Teaching Hospital, Ghana

Modeling the Survival of Retrospective Clinical Data from Prostate Cancer Patients in Komfo Anokye Teaching Hospital, Ghana Internatonal Journal of Appled Scence and Technology Vol. 5, No. 6; December 2015 Modelng the Survval of Retrospectve Clncal Data from Prostate Cancer Patents n Komfo Anokye Teachng Hosptal, Ghana Asedu-Addo,

More information

Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity Internatonal Journal of Intellgent Systems and Applcatons n Engneerng Advanced Technology and Scence ISSN:2147-67992147-6799 http://jsae.atscence.org/ Orgnal Research Paper Comparson among Feature Encodng

More information

Research Article Computational Analysis of Specific MicroRNA Biomarkers for Noninvasive Early Cancer Detection

Research Article Computational Analysis of Specific MicroRNA Biomarkers for Noninvasive Early Cancer Detection Hndaw BoMed Research Internatonal Volume 0, Artcle ID 00, pages https://do.org/0./0/00 Research Artcle Computatonal Analyss of Specfc McroRNA Bomarkers for Nonnvasve Early Detecton Tanc Song, Yanchun Lang,,

More information

Using a Wavelet Representation for Classification of Movement in Bed

Using a Wavelet Representation for Classification of Movement in Bed Usng a Wavelet Representaton for Classfcaton of Movement n Bed Adrana Morell Adam Depto. de Matemátca e Estatístca Unversdade de Caxas do Sul Caxas do Sul RS E-mal: amorell@ucs.br André Gustavo Adam Depto.

More information

A GEOGRAPHICAL AND STATISTICAL ANALYSIS OF LEUKEMIA DEATHS RELATING TO NUCLEAR POWER PLANTS. Whitney Thompson, Sarah McGinnis, Darius McDaniel,

A GEOGRAPHICAL AND STATISTICAL ANALYSIS OF LEUKEMIA DEATHS RELATING TO NUCLEAR POWER PLANTS. Whitney Thompson, Sarah McGinnis, Darius McDaniel, A GEOGRAPHICAL AD STATISTICAL AALYSIS OF LEUKEMIA DEATHS RELATIG TO UCLEAR POWER PLATS Whtney Thompson, Sarah McGnns, Darus McDanel, Jean Sexton, Rebecca Pettt, Sarah Anderson, Monca Jackson ABSTRACT:

More information

Feature Selection for Predicting Tumor Metastases in Microarray Experiments using Paired Design

Feature Selection for Predicting Tumor Metastases in Microarray Experiments using Paired Design Feature Selecton for Predctng Tumor Metastases n Mcroarray Experments usng Pared Desgn Qhua Tan 1,2, Mads Thomassen 1 and Torben A. Kruse 1 ORIGINAL RESEARCH 1 Department of Bochemstry, Pharmacology and

More information

(From the Gastroenterology Division, Cornell University Medical College, New York 10021)

(From the Gastroenterology Division, Cornell University Medical College, New York 10021) ROLE OF HEPATIC ANION-BINDING PROTEIN IN BROMSULPHTHALEIN CONJUGATION* BY N. KAPLOWITZ, I. W. PERC -ROBB,~ ANn N. B. JAVITT (From the Gastroenterology Dvson, Cornell Unversty Medcal College, New York 10021)

More information

Estimation for Pavement Performance Curve based on Kyoto Model : A Case Study for Highway in the State of Sao Paulo

Estimation for Pavement Performance Curve based on Kyoto Model : A Case Study for Highway in the State of Sao Paulo Estmaton for Pavement Performance Curve based on Kyoto Model : A Case Study for Kazuya AOKI, PASCO CORPORATION, Yokohama, JAPAN, Emal : kakzo603@pasco.co.jp Octávo de Souza Campos, Publc Servces Regulatory

More information

Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field Subject-Adaptve Real-Tme Sleep Stage Classfcaton Based on Condtonal Random Feld Gang Luo, PhD, Wanl Mn, PhD IBM TJ Watson Research Center, Hawthorne, NY {luog, wanlmn}@usbmcom Abstract Sleep stagng s the

More information

EVALUATION OF BULK MODULUS AND RING DIAMETER OF SOME TELLURITE GLASS SYSTEMS

EVALUATION OF BULK MODULUS AND RING DIAMETER OF SOME TELLURITE GLASS SYSTEMS Chalcogende Letters Vol. 12, No. 2, February 2015, p. 67-74 EVALUATION OF BULK MODULUS AND RING DIAMETER OF SOME TELLURITE GLASS SYSTEMS R. EL-MALLAWANY a*, M.S. GAAFAR b, N. VEERAIAH c a Physcs Dept.,

More information

The Effect of Fish Farmers Association on Technical Efficiency: An Application of Propensity Score Matching Analysis

The Effect of Fish Farmers Association on Technical Efficiency: An Application of Propensity Score Matching Analysis The Effect of Fsh Farmers Assocaton on Techncal Effcency: An Applcaton of Propensty Score Matchng Analyss Onumah E. E, Esslfe F. L, and Asumng-Brempong, S 15 th July, 2016 Background and Motvaton Outlne

More information

A Geometric Approach To Fully Automatic Chromosome Segmentation

A Geometric Approach To Fully Automatic Chromosome Segmentation A Geometrc Approach To Fully Automatc Chromosome Segmentaton Shervn Mnaee ECE Department New York Unversty Brooklyn, New York, USA shervn.mnaee@nyu.edu Mehran Fotouh Computer Engneerng Department Sharf

More information

Saeed Ghanbari, Seyyed Mohammad Taghi Ayatollahi*, Najaf Zare

Saeed Ghanbari, Seyyed Mohammad Taghi Ayatollahi*, Najaf Zare DOI:http://dx.do.org/10.7314/APJCP.2015.16.14.5655 and Anthracyclne- Breast Cancer Treatment and Survval n the Eastern Medterranean and Asa: a Meta-analyss RESEARCH ARTICLE Comparng Role of Two Chemotherapy

More information

Resampling Methods for the Area Under the ROC Curve

Resampling Methods for the Area Under the ROC Curve Resamplng ethods for the Area Under the ROC Curve Andry I. Bandos AB6@PITT.EDU Howard E. Rockette HERBST@PITT.EDU Department of Bostatstcs, Graduate School of Publc Health, Unversty of Pttsburgh, Pttsburgh,

More information

A Linear Regression Model to Detect User Emotion for Touch Input Interactive Systems

A Linear Regression Model to Detect User Emotion for Touch Input Interactive Systems 2015 Internatonal Conference on Affectve Computng and Intellgent Interacton (ACII) A Lnear Regresson Model to Detect User Emoton for Touch Input Interactve Systems Samt Bhattacharya Dept of Computer Scence

More information

Dr.S.Sumathi 1, Mrs.V.Agalya 2 Mahendra Engineering College, Mahendhirapuri, Mallasamudram

Dr.S.Sumathi 1, Mrs.V.Agalya 2 Mahendra Engineering College, Mahendhirapuri, Mallasamudram Detecton Of Myocardal Ischema In ECG Sgnals Usng Support Vector Machne Dr.S.Sumath 1, Mrs.V.Agalya Mahendra Engneerng College, Mahendhrapur, Mallasamudram Abstract--Ths paper presents an ntellectual dagnoss

More information

Appendix for. Institutions and Behavior: Experimental Evidence on the Effects of Democracy

Appendix for. Institutions and Behavior: Experimental Evidence on the Effects of Democracy Appendx for Insttutons and Behavor: Expermental Evdence on the Effects of Democrac 1. Instructons 1.1 Orgnal sessons Welcome You are about to partcpate n a stud on decson-makng, and ou wll be pad for our

More information

TOPICS IN HEALTH ECONOMETRICS

TOPICS IN HEALTH ECONOMETRICS TOPICS IN HEALTH ECONOMETRICS By VIDHURA SENANI BANDARA WIJAYAWARDHANA TENNEKOON A dssertaton submtted n partal fulfllment of the requrements for the degree of DOCTOR OF PHILOSOPHY WASHINGTON STATE UNIVERSITY

More information

A-UNIFAC Modeling of Binary and Multicomponent Phase Equilibria of Fatty Esters+Water+Methanol+Glycerol

A-UNIFAC Modeling of Binary and Multicomponent Phase Equilibria of Fatty Esters+Water+Methanol+Glycerol -UNIFC Modelng of Bnary and Multcomponent Phase Equlbra of Fatty Esters+Water+Methanol+Glycerol N. Garrdo a, O. Ferrera b, R. Lugo c, J.-C. de Hemptnne c, M. E. Macedo a, S.B. Bottn d,* a Department of

More information

ARTICLE IN PRESS Neuropsychologia xxx (2010) xxx xxx

ARTICLE IN PRESS Neuropsychologia xxx (2010) xxx xxx Neuropsychologa xxx (200) xxx xxx Contents lsts avalable at ScenceDrect Neuropsychologa journal homepage: www.elsever.com/locate/neuropsychologa Storage and bndng of object features n vsual workng memory

More information

What Determines Attitude Improvements? Does Religiosity Help?

What Determines Attitude Improvements? Does Religiosity Help? Internatonal Journal of Busness and Socal Scence Vol. 4 No. 9; August 2013 What Determnes Atttude Improvements? Does Relgosty Help? Madhu S. Mohanty Calforna State Unversty-Los Angeles Los Angeles, 5151

More information

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches Towards Predcton of Radaton Pneumonts Arsng from Lung Cancer Patents Usng Machne Learnng Approaches Jung Hun Oh, Adtya Apte, Rawan Al-Loz, Jeffrey Bradley, Issam El Naqa * Dvson of Bonformatcs and Outcomes

More information

Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods

Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods Research Artcle Receved 30 September 2009, Accepted 15 March 2010 Publshed onlne n Wley Onlne Lbrary (wleyonlnelbrary.com) DOI: 10.1002/sm.3941 Estmatng the dstrbuton of the wndow perod for recent HIV

More information

Effects of Estrogen Contamination on Human Cells: Modeling and Prediction Based on Michaelis-Menten Kinetics 1

Effects of Estrogen Contamination on Human Cells: Modeling and Prediction Based on Michaelis-Menten Kinetics 1 J. Water Resource and Protecton, 009,, 6- do:0.6/warp.009.500 Publshed Onlne ovember 009 (http://www.scrp.org/ournal/warp) Effects of Estrogen Contamnaton on Human Cells: Modelng and Predcton Based on

More information

Drug Prescription Behavior and Decision Support Systems

Drug Prescription Behavior and Decision Support Systems Drug Prescrpton Behavor and Decson Support Systems ABSTRACT Adverse drug events plague the outcomes of health care servces. In ths research, we propose a clncal learnng model that ncorporates the use of

More information

Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data

Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data Unobserved Heterogenety and the Statstcal Analyss of Hghway Accdent Data Fred L. Mannerng Professor of Cvl and Envronmental Engneerng Courtesy Department of Economcs Unversty of South Florda 4202 E. Fowler

More information

Non-linear Multiple-Cue Judgment Tasks

Non-linear Multiple-Cue Judgment Tasks Non-lnear Multple-Cue Tasks Anna-Carn Olsson (anna-carn.olsson@psy.umu.se) Department of Psychology, Umeå Unversty SE-09 87, Umeå, Sweden Tommy Enqvst (tommy.enqvst@psyk.uu.se) Department of Psychology,

More information

NUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 TIANHONG ZHOU

NUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 TIANHONG ZHOU NUMERICAL COMPARISONS OF BIOASSAY METHODS IN ESTIMATING LC50 by TIANHONG ZHOU B.S., Chna Agrcultural Unversty, 2003 M.S., Chna Agrcultural Unversty, 2006 A THESIS submtted n partal fulfllment of the requrements

More information

Boosting for tumor classification with gene expression data. Seminar für Statistik, ETH Zürich, CH-8092, Switzerland

Boosting for tumor classification with gene expression data. Seminar für Statistik, ETH Zürich, CH-8092, Switzerland BIOINFORMATICS Vol. 19 no. 9 2003, pages 1061 1069 DOI: 10.1093/bonformatcs/btf867 Boostng for tumor classfcaton wth gene expresson data Marcel Dettlng and Peter Bühlmann Semnar für Statstk, ETH Zürch,

More information

Active Affective State Detection and User Assistance with Dynamic Bayesian Networks. Xiangyang Li, Qiang Ji

Active Affective State Detection and User Assistance with Dynamic Bayesian Networks. Xiangyang Li, Qiang Ji Actve Affectve State Detecton and User Assstance wth Dynamc Bayesan Networks Xangyang L, Qang J Electrcal, Computer, and Systems Engneerng Department Rensselaer Polytechnc Insttute, 110 8th Street, Troy,

More information

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

TTCA: an R package for the identification of differentially expressed genes in time course microarray data Albrecht et al. BMC Bonformatcs (2017) 18:33 DOI 10.1186/s12859-016-1440-8 METHODOLOGY ARTICLE Open Access TTCA: an R package for the dentfcaton of dfferentally expressed genes n tme course mcroarray data

More information

CONSTRUCTION OF STOCHASTIC MODEL FOR TIME TO DENGUE VIRUS TRANSMISSION WITH EXPONENTIAL DISTRIBUTION

CONSTRUCTION OF STOCHASTIC MODEL FOR TIME TO DENGUE VIRUS TRANSMISSION WITH EXPONENTIAL DISTRIBUTION Internatonal Journal of Pure and Appled Mathematcal Scences. ISSN 97-988 Volume, Number (7), pp. 3- Research Inda Publcatons http://www.rpublcaton.com ONSTRUTION OF STOHASTI MODEL FOR TIME TO DENGUE VIRUS

More information

A Support Vector Machine Classifier based on Recursive Feature Elimination for Microarray Data in Breast Cancer Characterization. Abstract.

A Support Vector Machine Classifier based on Recursive Feature Elimination for Microarray Data in Breast Cancer Characterization. Abstract. A Support Vector Machne Classfer based on Recursve Feature Elmnaton for Mcroarray Data n Breast Cancer Characterzaton. R.Campann, D. Dongovann, E. Iamper, N. Lanconell, G. Palermo, M. Roffll, A. Rccard

More information

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning

Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning Amercan Journal of Appled Scences Orgnal Research Paper Classfcaton of Breast Tumor n Mammogram Images Usng Unsupervsed Feature Learnng 1 Adarus M. Ibrahm, 1 Baharum Baharudn, 1 Abas Md Sad and 2 P.N.

More information

CLUSTERING is always popular in modern technology

CLUSTERING is always popular in modern technology Max-Entropy Feed-Forward Clusterng Neural Network Han Xao, Xaoyan Zhu arxv:1506.03623v1 [cs.lg] 11 Jun 2015 Abstract The outputs of non-lnear feed-forward neural network are postve, whch could be treated

More information

Research Article Statistical Segmentation of Regions of Interest on a Mammographic Image

Research Article Statistical Segmentation of Regions of Interest on a Mammographic Image Hndaw Publshng Corporaton EURASIP Journal on Advances n Sgnal Processng Volume 2007, Artcle ID 49482, 8 pages do:10.1155/2007/49482 Research Artcle Statstcal Segmentaton of Regons of Interest on a Mammographc

More information

A comparison of statistical methods in interrupted time series analysis to estimate an intervention effect

A comparison of statistical methods in interrupted time series analysis to estimate an intervention effect Peer revew stream A comparson of statstcal methods n nterrupted tme seres analyss to estmate an nterventon effect a,b, J.J.J., Walter c, S., Grzebeta a, R. & Olver b, J. a Transport and Road Safety, Unversty

More information

THIS IS AN OFFICIAL NH DHHS HEALTH ALERT

THIS IS AN OFFICIAL NH DHHS HEALTH ALERT THIS IS AN OFFICIAL NH DHHS HEALTH ALERT Dstrbuted by the NH Health Alert Network Health.Alert@dhhs.nh.gov August 26, 2016 1430 EDT (2:30 PM EDT) NH-HAN 20160826 Recommendatons for Accurate Dagnoss of

More information

The Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis

The Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis The Lmts of Indvdual Identfcaton from Sample Allele Frequences: Theory and Statstcal Analyss Peter M. Vsscher 1 *, Wllam G. Hll 2 1 Queensland Insttute of Medcal Research, Brsbane, Australa, 2 Insttute

More information

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi Unversty of Pennsylvana ScholarlyCommons PSC Workng Paper Seres 7-29-20 HIV/AIDS-related Expectatons and Rsky Sexual Behavor n Malaw Adelne Delavande RAND Corporaton, Nova School of Busness and Economcs

More information

THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II

THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II Name: Date: THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II The normal dstrbuton can be used n ncrements other than half-standard devatons. In fact, we can use ether our calculators or tables

More information

A Support Vector Machine Classifier based on Recursive Feature Elimination for Microarray Data in Breast Cancer Characterization. Abstract.

A Support Vector Machine Classifier based on Recursive Feature Elimination for Microarray Data in Breast Cancer Characterization. Abstract. A Support Vector Machne Classfer based on Recursve Feature Elmnaton for Mcroarray Data n Breast Cancer Characterzaton. R.Campann, D. Dongovann, N. Lanconell, G. Palermo, A. Rccard, M. Roffll Dpartmento

More information

Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase

Normal variation in the length of the luteal phase of the menstrual cycle: identification of the short luteal phase Brtsh Journal of Obstetrcs and Gvnaecologjl July 1984, Vol. 9 1, pp. 685-689 Normal varaton n the length of the luteal phase of the menstrual cycle: dentfcaton of the short luteal phase ELIZABETH A. LENTON,

More information

Using the Perpendicular Distance to the Nearest Fracture as a Proxy for Conventional Fracture Spacing Measures

Using the Perpendicular Distance to the Nearest Fracture as a Proxy for Conventional Fracture Spacing Measures Usng the Perpendcular Dstance to the Nearest Fracture as a Proxy for Conventonal Fracture Spacng Measures Erc B. Nven and Clayton V. Deutsch Dscrete fracture network smulaton ams to reproduce dstrbutons

More information

Combined Temporal and Spatial Filter Structures for CDMA Systems

Combined Temporal and Spatial Filter Structures for CDMA Systems Combned Temporal and Spatal Flter Structures for CDMA Systems Ayln Yener WINLAB, Rutgers Unversty yener@wnlab.rutgers.edu Roy D. Yates WINLAB, Rutgers Unversty ryates@wnlab.rutgers.edu Sennur Ulukus AT&T

More information

Project title: Mathematical Models of Fish Populations in Marine Reserves

Project title: Mathematical Models of Fish Populations in Marine Reserves Applcaton for Fundng (Malaspna Research Fund) Date: November 0, 2005 Project ttle: Mathematcal Models of Fsh Populatons n Marne Reserves Dr. Lev V. Idels Unversty College Professor Mathematcs Department

More information

A Computer-aided System for Discriminating Normal from Cancerous Regions in IHC Liver Cancer Tissue Images Using K-means Clustering*

A Computer-aided System for Discriminating Normal from Cancerous Regions in IHC Liver Cancer Tissue Images Using K-means Clustering* A Computer-aded System for Dscrmnatng Normal from Cancerous Regons n IHC Lver Cancer Tssue Images Usng K-means Clusterng* R. M. CHEN 1, Y. J. WU, S. R. JHUANG, M. H. HSIEH, C. L. KUO, Y. L. MA Department

More information

Encoding processes, in memory scanning tasks

Encoding processes, in memory scanning tasks vlemory & Cognton 1976,4 (5), 501 506 Encodng processes, n memory scannng tasks JEFFREY O. MILLER and ROBERT G. PACHELLA Unversty of Mchgan, Ann Arbor, Mchgan 48101, Three experments are presented that

More information

*VALLIAPPAN Raman 1, PUTRA Sumari 2 and MANDAVA Rajeswari 3. George town, Penang 11800, Malaysia. George town, Penang 11800, Malaysia

*VALLIAPPAN Raman 1, PUTRA Sumari 2 and MANDAVA Rajeswari 3. George town, Penang 11800, Malaysia. George town, Penang 11800, Malaysia 38 A Theoretcal Methodology and Prototype Implementaton for Detecton Segmentaton Classfcaton of Dgtal Mammogram Tumor by Machne Learnng and Problem Solvng *VALLIAPPA Raman, PUTRA Sumar 2 and MADAVA Rajeswar

More information

Statistical Analysis on Infectious Diseases in Dubai, UAE

Statistical Analysis on Infectious Diseases in Dubai, UAE Internatonal Journal of Preventve Medcne Research Vol. 1, No. 4, 015, pp. 60-66 http://www.ascence.org/journal/jpmr Statstcal Analyss on Infectous Dseases 1995-013 n Duba, UAE Khams F. G. 1, Hussan H.

More information

Available online at ScienceDirect. Procedia Computer Science 46 (2015 )

Available online at   ScienceDirect. Procedia Computer Science 46 (2015 ) Avalable onlne at www.scencedrect.com ScenceDrect Proceda Computer Scence 46 (215 ) 1762 1769 Internatonal Conference on Informaton and Communcaton Technologes (ICICT 214) Automatc Characterzaton of Bengn

More information

Appendix F: The Grant Impact for SBIR Mills

Appendix F: The Grant Impact for SBIR Mills Appendx F: The Grant Impact for SBIR Mlls Asmallsubsetofthefrmsnmydataapplymorethanonce.Ofthe7,436applcant frms, 71% appled only once, and a further 14% appled twce. Wthn my data, seven companes each submtted

More information

Analysis of Correlated Recurrent and Terminal Events Data in SAS Li Lu 1, Chenwei Liu 2

Analysis of Correlated Recurrent and Terminal Events Data in SAS Li Lu 1, Chenwei Liu 2 Statstcs & Analyss Analyss of Correlated Recurrent and ermnal Events Data n SAS L Lu 1, Chenwe Lu 2 1 he EMMES Corporaton, Rockvlle, MD 2 Core Genotypng Faclty, Dvson of Cancer Epdemology and Genetcs,

More information

Economic crisis and follow-up of the conditions that define metabolic syndrome in a cohort of Catalonia,

Economic crisis and follow-up of the conditions that define metabolic syndrome in a cohort of Catalonia, Economc crss and follow-up of the condtons that defne metabolc syndrome n a cohort of Catalona, 2005-2012 Laa Maynou 1,2,3, Joan Gl 4, Gabrel Coll-de-Tuero 5,2, Ton Mora 6, Carme Saurna 1,2, Anton Scras

More information

WHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS

WHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS WHO S ASSESSMENT OF HEALTH CARE INDUSTRY PERFORMANCE: RATING THE RANKINGS ELLIOTT PARKER and JEANNE WENDEL * Department of Economcs, Unversty of Nevada, Reno, NV, USA SUMMARY Ths paper examnes the econometrc

More information

Algorithms 2009, 2, ; doi: /a OPEN ACCESS

Algorithms 2009, 2, ; doi: /a OPEN ACCESS Algorthms 009,, 350-367; do:0.3390/a04350 OPEN ACCESS algorthms ISSN 999-4893 www.mdp.com/journal/algorthms Artcle CADrx for GBM Bran Tumors: Predctng Treatment Response from Changes n Dffuson-Weghted

More information

FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION

FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION computng@tanet.edu.te.ua www.tanet.edu.te.ua/computng ISSN 727-6209 Internatonal Scentfc Journal of Computng FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION Gábor Takács ), Béla Patak

More information

Investigation of zinc oxide thin film by spectroscopic ellipsometry

Investigation of zinc oxide thin film by spectroscopic ellipsometry VNU Journal of Scence, Mathematcs - Physcs 24 (2008) 16-23 Investgaton of znc oxde thn flm by spectroscopc ellpsometry Nguyen Nang Dnh 1, Tran Quang Trung 2, Le Khac Bnh 2, Nguyen Dang Khoa 2, Vo Th Ma

More information

Richard Williams Notre Dame Sociology Meetings of the European Survey Research Association Ljubljana,

Richard Williams Notre Dame Sociology   Meetings of the European Survey Research Association Ljubljana, Rchard Wllams Notre Dame Socology rwllam@nd.edu http://www.nd.edu/~rwllam Meetngs of the European Survey Research Assocaton Ljubljana, Slovena July 19, 2013 Comparng Logt and Probt Coeffcents across groups

More information

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi

HIV/AIDS-related Expectations and Risky Sexual Behavior in Malawi HIV/AIDS-related Expectatons and Rsky Sexual Behavor n Malaw Adelne Delavande Unversty of Essex and RAND Corporaton Hans-Peter Kohler Unversty of Pennsylvanna January 202 Abstract We use probablstc expectatons

More information

N-back Training Task Performance: Analysis and Model

N-back Training Task Performance: Analysis and Model N-back Tranng Task Performance: Analyss and Model J. Isaah Harbson (jharb@umd.edu) Center for Advanced Study of Language and Department of Psychology, Unversty of Maryland 7005 52 nd Avenue, College Park,

More information