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@article{Li17102013,
	Abstract = {Motivation: Systematic identification of microRNA (miRNA) targets remains a challenge. miRNA overexpression coupled with genome- wide expression profiling is a promising new approach and calls for a new method that integrates expression and sequence information.Results: We developed a probabilistic scoring method called TargetScore. TargetScore infers miRNA targets as the transformed fold-changes weighted by the Bayesian posteriors given observed target features. To this end, we compiled 84 datasets from GEO corresponding to 77 human tissue or cells and 113 distinct transfected miRNAs. Comparing with other methods, TargetScore achieves significantly higher accuracy in identifying known targets in most tests. Moreover, the confidence targets from TargetScore exhibit comparable protein down-regulation and are more significantly enriched for Gene Ontology terms. Using targetScore, we explored oncomir-oncogenes network and predicted several potential cancer-related miRNA-mRNA interactions.Availability: TargetScore is available at Bioconductor: http://www.bioconductor.org/packages/devel/bioc/html/TargetScore.html.Contact: yueli@cs.toronto.edu; zhaolei.zhang@utoronto.ca},
	Author = {Li, Yue and Goldenberg, Anna and Wong, Ka-Chun and Zhang, Zhaolei},
	Date-Added = {2013-10-18 02:30:51 +0000},
	Date-Modified = {2013-10-18 02:30:51 +0000},
	Doi = {10.1093/bioinformatics/btt599},
	Eprint = {http://bioinformatics.oxfordjournals.org/content/early/2013/10/17/bioinformatics.btt599.full.pdf+html},
	Journal = {Bioinformatics},
	Title = {A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information},
	Url = {http://bioinformatics.oxfordjournals.org/content/early/2013/10/17/bioinformatics.btt599.abstract},
	Year = {2013},
	Bdsk-Url-1 = {http://bioinformatics.oxfordjournals.org/content/early/2013/10/17/bioinformatics.btt599.abstract},
	Bdsk-Url-2 = {http://dx.doi.org/10.1093/bioinformatics/btt599}}

@article{Anders:2010yq,
	Author = {Simon Anders and Wolfgang Huber},
	Date-Added = {2013-07-13 14:00:15 +0000},
	Date-Modified = {2013-07-13 14:00:17 +0000},
	Doi = {10.1186/gb-2010-11-10-r106},
	Journal = {Genome Biology},
	Pages = {R106},
	Title = {Differential expression analysis for sequence count data},
	Url = {http://genomebiology.com/2010/11/10/R106/},
	Volume = {11},
	Year = {2010},
	Bdsk-Url-1 = {http://genomebiology.com/2010/11/10/R106/},
	Bdsk-Url-2 = {http://dx.doi.org/10.1186/gb-2010-11-10-r106}}

@incollection{Smyth:2005fj,
	Address = {New York},
	Author = {Gordon K Smyth},
	Booktitle = {Bioinformatics and Computational Biology Solutions Using {R} and Bioconductor},
	Date-Added = {2013-07-13 13:44:57 +0000},
	Date-Modified = {2013-07-13 13:44:59 +0000},
	Editor = {R. Gentleman and V. Carey and S. Dudoit and R. Irizarry and W. Huber},
	Pages = {397--420},
	Publisher = {Springer},
	Title = {Limma: linear models for microarray data},
	Year = {2005}}

@article{Tsang:2007hl,
	Author = {Tsang, John and Zhu, Jun and van Oudenaarden, Alexander},
	Date-Added = {2013-07-01 00:17:33 +0000},
	Date-Modified = {2013-07-01 00:17:33 +0000},
	Journal = {Molecular Cell},
	Month = jun,
	Number = {5},
	Pages = {753--767},
	Title = {{MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals.}},
	Volume = {26},
	Year = {2007}}

@article{Lu:2012ji,
	Author = {Lu, J and Clark, A G},
	Date-Added = {2013-06-28 22:11:11 +0000},
	Date-Modified = {2013-06-28 22:11:11 +0000},
	Journal = {Genome Research},
	Month = mar,
	Title = {{Impact of microRNA regulation on variation in human gene expression}},
	Year = {2012}}

@article{Troyanskaya:2001uh,
	Author = {Troyanskaya, O and Cantor, M and Sherlock, G and Brown, P and Hastie, T and Tibshirani, R and Botstein, D and Altman, R B},
	Date-Added = {2013-06-28 16:39:23 +0000},
	Date-Modified = {2013-06-28 16:39:23 +0000},
	Journal = {Bioinformatics (Oxford, England)},
	Month = jun,
	Number = {6},
	Pages = {520--525},
	Title = {{Missing value estimation methods for DNA microarrays.}},
	Volume = {17},
	Year = {2001}}

@article{Anders:2011ci,
	Author = {Anders, G and Mackowiak, S D and Jens, M and Maaskola, J and Kuntzagk, A and Rajewsky, N and Landthaler, M and Dieterich, C},
	Date-Added = {2013-06-28 02:42:18 +0000},
	Date-Modified = {2013-06-28 02:42:18 +0000},
	Journal = {Nucleic Acids Research},
	Month = dec,
	Number = {D1},
	Pages = {D180--D186},
	Title = {{doRiNA: a database of RNA interactions in post-transcriptional regulation}},
	Volume = {40},
	Year = {2011}}

@article{Liu:2010di,
	Author = {Liu, Hui and Yue, Dong and Chen, Yidong and Gao, Shou-Jiang and Huang, Yufei},
	Date-Added = {2013-06-27 01:06:45 +0000},
	Date-Modified = {2013-06-27 01:06:45 +0000},
	Journal = {BMC Bioinformatics},
	Number = {1},
	Pages = {476},
	Title = {{Improving performance of mammalian microRNA target prediction}},
	Volume = {11},
	Year = {2010}}

@article{Hsu:2011ed,
	Author = {Hsu, Sheng-Da and Lin, Feng-Mao and Wu, Wei-Yun and Liang, Chao and Huang, Wei-Chih and Chan, Wen-Ling and Tsai, Wen-Ting and Chen, Goun-Zhou and Lee, Chia-Jung and Chiu, Chih-Min and Chien, Chia-Hung and Wu, Ming-Chia and Huang, Chi-Ying and Tsou, Ann-Ping and Huang, Hsien-Da},
	Date-Added = {2013-06-27 00:48:27 +0000},
	Date-Modified = {2013-06-27 00:48:27 +0000},
	Journal = {Nucleic acids research},
	Month = jan,
	Number = {Database issue},
	Pages = {D163--9},
	Title = {{miRTarBase: a database curates experimentally validated microRNA-target interactions.}},
	Volume = {39},
	Year = {2011}}

@article{Khan:2009ki,
	Author = {Khan, Aly A and Betel, Doron and Miller, Martin L and Sander, Chris and Leslie, Christina S and Marks, Debora S},
	Date-Added = {2013-04-22 01:56:40 +0000},
	Date-Modified = {2013-04-22 01:56:40 +0000},
	Journal = {Nature Biotechnology},
	Month = may,
	Title = {{Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs}},
	Year = {2009}}

@article{Selbach:2008hx,
	Author = {Selbach, Matthias and Schwanh{\"a}usser, Bj{\"o}rn and Thierfelder, Nadine and Fang, Zhuo and Khanin, Raya and Rajewsky, Nikolaus},
	Date-Added = {2013-04-21 16:18:59 +0000},
	Date-Modified = {2013-04-21 16:18:59 +0000},
	Journal = {Nature},
	Month = sep,
	Number = {7209},
	Pages = {58--63},
	Title = {{Widespread changes in protein synthesis induced by microRNAs.}},
	Volume = {455},
	Year = {2008}}

@article{Lorenz:2011it,
	Author = {Lorenz, Ronny and Bernhart, Stephan H and H{\"o}ner Zu Siederdissen, Christian and Tafer, Hakim and Flamm, Christoph and Stadler, Peter F and Hofacker, Ivo L},
	Date-Added = {2013-04-21 16:04:46 +0000},
	Date-Modified = {2013-04-21 16:04:46 +0000},
	Journal = {Algorithms for molecular biology : AMB},
	Pages = {26},
	Title = {{ViennaRNA Package 2.0.}},
	Volume = {6},
	Year = {2011}}

@article{Alexiou:2009jq,
	Author = {Alexiou, Panagiotis and Maragkakis, Manolis and Papadopoulos, Giorgos L and Reczko, Martin and Hatzigeorgiou, Artemis G},
	Date-Added = {2013-04-21 15:53:20 +0000},
	Date-Modified = {2013-04-21 15:53:20 +0000},
	Journal = {Bioinformatics (Oxford, England)},
	Month = dec,
	Number = {23},
	Pages = {3049--3055},
	Title = {{Lost in translation: an assessment and perspective for computational microRNA target identification.}},
	Volume = {25},
	Year = {2009}}

@article{Vergoulis:2012dk,
	Author = {Vergoulis, Thanasis and Vlachos, Ioannis S and Alexiou, Panagiotis and Georgakilas, George and Maragkakis, Manolis and Reczko, Martin and Gerangelos, Stefanos and Koziris, Nectarios and Dalamagas, Theodore and Hatzigeorgiou, Artemis G},
	Date-Added = {2013-04-21 15:41:54 +0000},
	Date-Modified = {2013-04-21 15:41:54 +0000},
	Journal = {Nucleic acids research},
	Month = jan,
	Number = {Database issue},
	Pages = {D222--9},
	Title = {{TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support.}},
	Volume = {40},
	Year = {2012}}

@article{Kozomara:2011es,
	Author = {Kozomara, Ana and Griffiths-Jones, Sam},
	Date-Added = {2013-04-12 15:17:52 +0000},
	Date-Modified = {2013-04-12 15:17:52 +0000},
	Journal = {Nucleic acids research},
	Month = jan,
	Number = {Database issue},
	Pages = {D152--7},
	Title = {{miRBase: integrating microRNA annotation and deep-sequencing data.}},
	Volume = {39},
	Year = {2011}}

@article{Durinck:2009ki,
	Author = {Durinck, Steffen and Spellman, Paul T and Birney, Ewan and Huber, Wolfgang},
	Date-Added = {2013-04-11 22:12:32 +0000},
	Date-Modified = {2013-04-11 22:12:32 +0000},
	Journal = {Nature protocols},
	Number = {8},
	Pages = {1184--1191},
	Title = {{Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.}},
	Volume = {4},
	Year = {2009}}

@article{Toedling:2007fk,
	Author = {Joern Toedling and Oleg Sklyar and Tammo Krueger and Jenny J Fischer and Silke Sperling and Wolfgang Huber},
	Date-Added = {2013-04-11 14:55:52 +0000},
	Date-Modified = {2013-04-11 14:55:54 +0000},
	Journal = {BMC Bioinformatics},
	Pages = {221},
	Title = {Ringo - an {R/Bioconductor} package for analyzing {ChIP-chip} readouts},
	Volume = {8},
	Year = {2007}}

@article{Saito:2012ie,
	Author = {Saito, Takaya and S{\ae}trom, P{\aa}l},
	Date-Added = {2013-03-11 01:23:53 +0000},
	Date-Modified = {2013-03-11 01:23:53 +0000},
	Journal = {Silence},
	Month = feb,
	Number = {1},
	Pages = {3},
	Title = {{Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments}},
	Volume = {3},
	Year = {2012}}

@article{Larsson:2010ks,
	Author = {Larsson, Erik and Sander, Chris and Marks, Debora},
	Date-Added = {2013-03-11 01:23:42 +0000},
	Date-Modified = {2013-03-11 01:23:42 +0000},
	Journal = {Molecular systems biology},
	Month = nov,
	Pages = {1--9},
	Title = {{mRNA turnover rate limits siRNA and microRNA efficacy}},
	Volume = {6},
	Year = {2010}}

@article{Li:2009tv,
	Author = {Li, Jingjing and Min, Renqiang and Bonner, Anthony and Zhang, Zhaolei},
	Date-Added = {2013-03-10 23:39:04 +0000},
	Date-Modified = {2013-03-10 23:39:04 +0000},
	Journal = {Journal of bioinformatics and computational biology},
	Month = dec,
	Number = {6},
	Pages = {955--972},
	Title = {{A probabilistic framework to improve microrna target prediction by incorporating proteomics data.}},
	Volume = {7},
	Year = {2009}}

@article{Stingo:2010wq,
	Author = {Stingo, F C and Chen, Y A and Vannucci, M and Barrier, M and Mirkes, P E},
	Date-Added = {2013-03-10 23:33:59 +0000},
	Date-Modified = {2013-03-10 23:33:59 +0000},
	Journal = {The Annals of Applied Statistics},
	Number = {4},
	Pages = {2024--2048},
	Title = {{A Bayesian graphical modeling approach to microRNA regulatory network inference}},
	Volume = {4},
	Year = {2010}}

@article{Su:2011vg,
	Author = {Su, N and Wang, Y and Qian, M and Deng, M},
	Date-Added = {2013-03-10 23:33:16 +0000},
	Date-Modified = {2013-03-10 23:33:16 +0000},
	Journal = {Systems Biology (ISB), 2011 IEEE International Conference on},
	Pages = {219--224},
	Title = {{Predicting MicroRNA targets by integrating sequence and expression data in cancer}},
	Year = {2011}}

@article{Le:2011wx,
	Author = {Le, H S and Bar-Joseph, Z},
	Date-Added = {2013-03-10 23:33:07 +0000},
	Date-Modified = {2013-03-10 23:33:07 +0000},
	Journal = {Advances in Neural Information Processing Systems, to appear},
	Title = {{Inferring interaction networks using the ibp applied to microrna target prediction}},
	Year = {2011}}

@article{Huang:2007do,
	Author = {Huang, Jim C and Morris, Quaid D and Frey, Brendan J},
	Date-Added = {2013-03-10 23:32:54 +0000},
	Date-Modified = {2013-03-10 23:32:54 +0000},
	Journal = {Journal of Computational Biology},
	Month = jun,
	Number = {5},
	Pages = {550--563},
	Title = {{Bayesian Inference of MicroRNA Targets from Sequence and Expression Data}},
	Volume = {14},
	Year = {2007}}

@article{Marbach:2012hw,
	Author = {Marbach, Daniel and Costello, James C and K{\"u}ffner, Robert and Vega, Nicole M and Prill, Robert J and Camacho, Diogo M and Allison, Kyle R and Aderhold, Andrej and Allison, Kyle R and ... and Stolovitzky, Gustavo},
	Date-Added = {2013-03-10 20:26:53 +0000},
	Date-Modified = {2013-03-10 20:31:44 +0000},
	Journal = {Nature Methods},
	Month = jul,
	Number = {8},
	Pages = {796--804},
	Title = {{Wisdom of crowds for robust gene network inference}},
	Volume = {9},
	Year = {2012}}

@article{Nielsen:2007jv,
	Author = {Nielsen, Cydney B and Shomron, Noam and Sandberg, Rickard and Hornstein, Eran and Kitzman, Jacob and Burge, Christopher B},
	Date-Added = {2013-03-10 19:39:21 +0000},
	Date-Modified = {2013-03-10 19:39:21 +0000},
	Journal = {RNA (New York, NY)},
	Month = nov,
	Number = {11},
	Pages = {1894--1910},
	Title = {{Determinants of targeting by endogenous and exogenous microRNAs and siRNAs.}},
	Volume = {13},
	Year = {2007}}

@article{Arvey:2010ev,
	Author = {Arvey, Aaron and Larsson, Erik and Sander, Chris and Leslie, Christina S and Marks, Debora S},
	Date-Added = {2013-03-10 16:28:16 +0000},
	Date-Modified = {2013-03-10 16:28:16 +0000},
	Journal = {Molecular systems biology},
	Month = apr,
	Pages = {1--7},
	Title = {{Target mRNA abundance dilutes microRNA and siRNA activity}},
	Volume = {6},
	Year = {2010}}

@article{Deng:2011fu,
	Author = {Deng, Nan and Puetter, Adriane and Zhang, Kun and Johnson, Kristen and Zhao, Zhiyu and Taylor, Christopher and Flemington, Erik K and Zhu, Dongxiao},
	Date-Added = {2013-03-10 16:23:06 +0000},
	Date-Modified = {2013-03-10 16:23:06 +0000},
	Journal = {Nucleic Acids Research},
	Month = may,
	Number = {9},
	Pages = {e61},
	Title = {{Isoform-level microRNA-155 target prediction using RNA-seq}},
	Volume = {39},
	Year = {2011}}

@article{Sethupathy:2006df,
	Author = {Sethupathy, Praveen and Megraw, Molly and Hatzigeorgiou, Artemis G},
	Date-Added = {2013-03-10 15:42:15 +0000},
	Date-Modified = {2013-03-10 15:42:15 +0000},
	Journal = {Nature Methods},
	Month = oct,
	Number = {11},
	Pages = {881--886},
	Title = {{A guide through present computational approaches for the identification of mammalian microRNA targets}},
	Volume = {3},
	Year = {2006}}

@article{Friedman:2009km,
	Author = {Friedman, Robin C and Farh, Kyle Kai-How and Burge, Christopher B and Bartel, David P},
	Date-Added = {2013-03-10 15:33:34 +0000},
	Date-Modified = {2013-03-10 15:33:34 +0000},
	Journal = {Genome Research},
	Month = jan,
	Number = {1},
	Pages = {92--105},
	Title = {{Most mammalian mRNAs are conserved targets of microRNAs.}},
	Volume = {19},
	Year = {2009}}

@article{Bartel:2009fh,
	Author = {Bartel, David P},
	Date-Added = {2013-03-10 15:32:17 +0000},
	Date-Modified = {2013-03-10 15:32:17 +0000},
	Journal = {Cell},
	Month = jan,
	Number = {2},
	Pages = {215--233},
	Title = {{MicroRNAs: Target Recognition and Regulatory Functions}},
	Volume = {136},
	Year = {2009}}

@article{Davis:2007fc,
	Author = {Davis, Sean and Meltzer, Paul S},
	Date-Added = {2013-03-10 15:05:34 +0000},
	Date-Modified = {2013-03-10 15:05:34 +0000},
	Journal = {Bioinformatics (Oxford, England)},
	Month = jul,
	Number = {14},
	Pages = {1846--1847},
	Title = {{GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.}},
	Volume = {23},
	Year = {2007}}

@article{Garcia:2011ec,
	Author = {Garcia, David M and Baek, Daehyun and Shin, Chanseok and Bell, George W and Grimson, Andrew and Bartel, David P},
	Date-Added = {2013-03-09 16:12:29 +0000},
	Date-Modified = {2013-03-09 16:12:29 +0000},
	Journal = {Nature structural {\&} molecular biology},
	Month = oct,
	Number = {10},
	Pages = {1139--1146},
	Title = {{Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs.}},
	Volume = {18},
	Year = {2011}}

@article{Grimson:2007cy,
	Author = {Grimson, Andrew and Farh, Kyle Kai-How and Johnston, Wendy K and Garrett-Engele, Philip and Lim, Lee P and Bartel, David P},
	Date-Added = {2013-03-09 16:12:29 +0000},
	Date-Modified = {2013-03-09 16:12:29 +0000},
	Journal = {Molecular Cell},
	Month = jul,
	Number = {1},
	Pages = {91--105},
	Title = {{MicroRNA targeting specificity in mammals: determinants beyond seed pairing.}},
	Volume = {27},
	Year = {2007}}

@article{Liu:2010cz,
	Author = {Liu, Hui and Yue, Dong and Zhang, Lin and Chen, Yidong and Gao, Shou-Jiang and Huang, Yufei},
	Date-Added = {2013-03-09 16:09:19 +0000},
	Date-Modified = {2013-03-09 16:09:19 +0000},
	Journal = {BMC Genomics},
	Month = dec,
	Number = {Suppl 3},
	Pages = {S12},
	Title = {{A Bayesian approach for identifying miRNA targets by combining sequence prediction and gene expression profiling}},
	Volume = {11},
	Year = {2010}}

@article{Kertesz:2007er,
	Author = {Kertesz, Michael and Iovino, Nicola and Unnerstall, Ulrich and Gaul, Ulrike and Segal, Eran},
	Date-Added = {2013-03-09 16:07:43 +0000},
	Date-Modified = {2013-03-09 16:07:43 +0000},
	Journal = {Nature Genetics},
	Month = sep,
	Number = {10},
	Pages = {1278--1284},
	Title = {{The role of site accessibility in microRNA target recognition}},
	Volume = {39},
	Year = {2007}}

@article{Enright:2004vw,
	Author = {Enright, A J and John, B and Gaul, U and Tuschl, T and Sander, C},
	Date-Added = {2013-03-09 16:03:19 +0000},
	Date-Modified = {2013-03-10 03:03:08 +0000},
	Journal = {Genome Biology},
	Number = {1},
	Pages = {R1-14},
	Title = {{MicroRNA targets in Drosophila}},
	Volume = {5},
	Year = {2004}}

@article{Krek:2005er,
	Annote = {PicTar},
	Author = {Krek, Azra and Gr{\"u}n, Dominic and Poy, Matthew N and Wolf, Rachel and Rosenberg, Lauren and Epstein, Eric J and MacMenamin, Philip and da Piedade, Isabelle and Gunsalus, Kristin C and Stoffel, Markus and Rajewsky, Nikolaus},
	Date-Added = {2013-03-09 16:02:29 +0000},
	Date-Modified = {2013-03-09 16:02:29 +0000},
	Journal = {Nature Genetics},
	Month = apr,
	Number = {5},
	Pages = {495--500},
	Title = {{Combinatorial microRNA target predictions}},
	Volume = {37},
	Year = {2005}}

@article{Lewis:2003uz,
	Annote = {TargetScan},
	Author = {Lewis, Benjamin P and Shih, I-hung and Jones-Rhoades, Matthew W and Bartel, David P and Burge, Christopher B},
	Date-Added = {2013-03-09 16:01:42 +0000},
	Date-Modified = {2013-03-09 16:01:42 +0000},
	Journal = {Cell},
	Month = dec,
	Number = {7},
	Pages = {787--798},
	Title = {{Prediction of mammalian microRNA targets.}},
	Volume = {115},
	Year = {2003}}

@article{Xiao:2009by,
	Author = {Xiao, Feifei and Zuo, Zhixiang and Cai, Guoshuai and Kang, Shuli and Gao, Xiaolian and Li, Tongbin},
	Date-Added = {2013-03-09 15:39:16 +0000},
	Date-Modified = {2013-03-09 15:39:16 +0000},
	Journal = {Nucleic Acids Research},
	Month = jan,
	Number = {Database issue},
	Pages = {D105--10},
	Title = {{miRecords: an integrated resource for microRNA-target interactions.}},
	Volume = {37},
	Year = {2009}}

@article{Baek:2008vd,
	Author = {Baek, D and Vill{\'e}n, J and Shin, C and Camargo, F D and Gygi, S P},
	Date-Added = {2013-03-09 15:38:39 +0000},
	Date-Modified = {2013-03-09 15:38:39 +0000},
	Journal = {Nature},
	Title = {{The impact of microRNAs on protein output}},
	Year = {2008}}

@article{Lim:2005cd,
	Author = {Lim, Lee P and Lau, Nelson C and Garrett-Engele, Philip and Grimson, Andrew and Schelter, Janell M and Castle, John and Bartel, David P and Linsley, Peter S and Johnson, Jason M},
	Date-Added = {2013-03-08 02:36:30 +0000},
	Date-Modified = {2013-03-08 02:36:30 +0000},
	Journal = {Nature},
	Month = feb,
	Number = {7027},
	Pages = {769--773},
	Title = {{Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.}},
	Volume = {433},
	Year = {2005}}

@manual{R-Development-Core-Team:2009vn,
	Address = {Vienna, Austria},
	Author = {{R Development Core Team}},
	Date-Added = {2010-12-20 18:14:34 -0500},
	Date-Modified = {2010-12-20 18:14:34 -0500},
	Note = {{ISBN} 3-900051-07-0},
	Organization = {R Foundation for Statistical Computing},
	Title = {R: A Language and Environment for Statistical Computing},
	Url = {http://www.R-project.org},
	Year = {2009},
	Bdsk-Url-1 = {http://www.R-project.org}}

@article{Roweis:2000,
	Author = {S Roweis{\ldots}},
	Date-Added = {2010-12-20 11:24:42 -0500},
	Date-Modified = {2010-12-20 11:26:35 -0500},
	Journal = {Science},
	Month = {Dec},
	Pages = {2323-2326},
	Title = {Nonlinear dimensionality reduction by locally linear embedding},
	Volume = {290},
	Year = {2000}}

@article{Dempster:1977p793,
	Author = {A Dempster and N Laird and D Rubin},
	Date-Added = {2010-12-20 00:40:52 -0500},
	Date-Modified = {2013-03-10 03:11:21 +0000},
	Journal = {Journal of the Royal Statistical Society. Series B},
	Number = {1},
	Pages = {1--38},
	Title = {Maximum likelihood from incomplete data via the EM algorithm},
	Volume = {39},
	Year = {1977}}

@article{Boutros:2009fk,
	Abstract = {Resectable non-small-cell lung cancer (NSCLC) patients have poor prognosis, with 30-50\% relapsing within 5 years. Current staging criteria do not fully capture the complexity of this disease. Survival could be improved by identification of those early-stage patients who are most likely to benefit from adjuvant therapy. Molecular classification by using mRNA expression profiles has led to multiple, poorly overlapping signatures. We hypothesized that differing statistical methodologies contribute to this lack of overlap. To test this hypothesis, we analyzed our previously published quantitative RT-PCR dataset with a semisupervised method. A 6-gene signature was identified and validated in 4 independent public microarray datasets that represent a range of tumor histologies and stages. This result demonstrated that at least 2 prognostic signatures can be derived from this single dataset. We next estimated the total number of prognostic signatures in this dataset with a 10-million-signature permutation study. Our 6-gene signature was among the top 0.02\% of signatures with maximum verifiability, reaffirming its efficacy. Importantly, this analysis identified 1,789 unique signatures, implying that our dataset contains >500,000 verifiable prognostic signatures for NSCLC. This result appears to rationalize the observed lack of overlap among reported NSCLC prognostic signatures.},
	Author = {Boutros, Paul C and Lau, Suzanne K and Pintilie, Melania and Liu, Ni and Shepherd, Frances A and Der, Sandy D and Tsao, Ming-Sound and Penn, Linda Z and Jurisica, Igor},
	Date-Added = {2010-12-20 00:39:28 -0500},
	Date-Modified = {2010-12-20 00:39:28 -0500},
	Doi = {10.1073/pnas.0809444106},
	Journal = {Proc Natl Acad Sci U S A},
	Journal-Full = {Proceedings of the National Academy of Sciences of the United States of America},
	Mesh = {Carcinoma, Non-Small-Cell Lung; Gene Expression Profiling; Humans; Lung Neoplasms; Prognosis; RNA, Messenger; Reverse Transcriptase Polymerase Chain Reaction},
	Month = {Feb},
	Number = {8},
	Pages = {2824-8},
	Pmc = {PMC2636731},
	Pmid = {19196983},
	Pst = {ppublish},
	Title = {Prognostic gene signatures for non-small-cell lung cancer},
	Volume = {106},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.0809444106}}

@article{Schwarz:1978,
	Author = {G Schwarz},
	Date-Added = {2010-12-20 00:34:50 -0500},
	Date-Modified = {2010-12-20 00:37:14 -0500},
	Journal = {The annals of statistics},
	Month = {Jan},
	Pages = {2907--2912},
	Title = {Estimating the dimension of a model},
	Volume = {6},
	Year = {1978}}

@article{Hubert:1985p857,
	Abstract = {Abstract: The problem of  two different  of a finite set of objects reappears continually in the clustering literature. We begin by review- ing a well-known measure of correspondence often attributed to Rand (1971), discuss the issue of correcting this index },
	Author = {L Hubert{\ldots}},
	Date-Added = {2010-12-20 00:13:20 -0500},
	Date-Modified = {2010-12-20 09:22:49 -0500},
	Journal = {Journal of classification},
	Month = {Jan},
	Pages = {193--218},
	Pmid = {776740494760102434related:IkrWWF2JxwoJ},
	Rating = {0},
	Title = {Comparing partitions},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p857},
	Url = {http://www.springerlink.com/index/x64124718341j1j0.pdf},
	Volume = {2},
	Year = {1985},
	Bdsk-Url-1 = {http://www.springerlink.com/index/x64124718341j1j0.pdf}}

@article{Rand:1971p831,
	Abstract = { of  measure clusterings. However, there is a natural exten- sion of this idea involving the comparison of two arbi- trary clusterings. Motivation of this measure follows three basic con- siderations. First,  is discrete in the sense that every },
	Author = {W Rand},
	Date-Added = {2010-12-19 23:57:37 -0500},
	Date-Modified = {2010-12-20 09:23:46 -0500},
	Journal = {Journal of the American Statistical association},
	Month = {Jan},
	Pages = {846-856},
	Pmid = {14051004653780245678related:rpzuhUky_8IJ},
	Rating = {0},
	Title = {Objective criteria for the evaluation of clustering methods},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p831},
	Url = {http://www.jstor.org/stable/2284239},
	Volume = {66},
	Year = {1971},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2284239}}

@article{Huber:2002wq,
	Abstract = {We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.},
	Author = {Huber, Wolfgang and von Heydebreck, Anja and S{\"u}ltmann, Holger and Poustka, Annemarie and Vingron, Martin},
	Date-Added = {2010-12-19 21:04:55 -0500},
	Date-Modified = {2010-12-19 21:04:55 -0500},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Analysis of Variance; Calibration; Data Interpretation, Statistical; Gene Expression Profiling; Likelihood Functions; Models, Genetic; Models, Statistical; Oligonucleotide Array Sequence Analysis; Reference Standards; Reproducibility of Results; Sensitivity and Specificity; Software},
	Pages = {S96-104},
	Pmid = {12169536},
	Title = {Variance stabilization applied to microarray data calibration and to the quantification of differential expression},
	Volume = {18 Suppl 1},
	Year = {2002},
	Bdsk-File-1 = { }}

@article{Bolstad:2003kx,
	Abstract = {MOTIVATION: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. RESULTS: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. AVAILABILITY: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html},
	Author = {Bolstad, B M and Irizarry, R A and Astrand, M and Speed, T P},
	Date-Added = {2010-12-19 21:03:54 -0500},
	Date-Modified = {2010-12-19 21:03:54 -0500},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Calibration; Models, Genetic; Molecular Probes; Nonlinear Dynamics; Oligonucleotide Array Sequence Analysis; Quality Control; Sequence Analysis, DNA; Stochastic Processes},
	Month = {Jan},
	Number = {2},
	Pages = {185-93},
	Pmid = {12538238},
	Pst = {ppublish},
	Title = {A comparison of normalization methods for high density oligonucleotide array data based on variance and bias},
	Volume = {19},
	Year = {2003}}

@article{Nishida:2010fk,
	Abstract = {When we analyze measured values by statistical techniques, we usually use a parametric method. It is necessary for measured values to show a normal distribution. Therefore, we must confirm that the distribution is normal, whereby a histogram shows that the distribution of data points is symmetrical above and below the mean. When measured values do not show a normal distribution, power transformation (square root transformation, logarithmic transformation) must be performed. We can evaluate the presence of normality by three methods: viewing a histogram, through skewness and kurtosis values, and Kolmogorov-Smirnov method's p-value. Because Kolmogorov-Smirnov's method is influenced by outliers, attention is necessary regarding the interpretation of p-values. For example, if we calculate reference intervals from clinical testing data, we calculate a parameter(mean and standard deviation) and set X +/- 2SD to upper and lower limits, respectively. When we evaluate the reference intervals, a range including 95\% of the central part of sample is important. Identification of the distribution type based on the diagonal linear pattern of normal quantile plots may be the most reliable in my experience.},
	Author = {Nishida, Toshinobu},
	Date-Added = {2010-12-19 21:02:42 -0500},
	Date-Modified = {2010-12-19 21:02:42 -0500},
	Journal = {Rinsho Byori},
	Journal-Full = {Rinsho byori. The Japanese journal of clinical pathology},
	Mesh = {Normal Distribution; Statistics as Topic},
	Month = {Oct},
	Number = {10},
	Pages = {990-7},
	Pmid = {21077289},
	Pst = {ppublish},
	Title = {[Data transformation and normalization]},
	Volume = {58},
	Year = {2010}}

@article{Akaike:1974p801,
	Abstract = {The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.},
	Author = {H Akaike},
	Date-Added = {2010-12-19 18:47:59 -0500},
	Date-Modified = {2010-12-19 18:47:59 -0500},
	Doi = {10.1109/TAC.1974.1100705},
	Journal = {Automatic Control, IEEE Transactions on},
	Number = {6},
	Pages = {716 -- 723},
	Pmid = {1100705},
	Rating = {0},
	Title = {A new look at the statistical model identification},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p801},
	Url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=1100705&queryText%253D%2528%2528A+new+look+at+Statistical+Model+Identification.%2529%2529%2526openedRefinements%253D*%2526sortType%253Ddesc_Publication+Year%2526matchBoolean%253Dtrue%2526rowsPerPage%253D50%2526searchField%253DSearch+All},
	Volume = {19},
	Year = {1974},
	Bdsk-Url-1 = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=1100705&queryText%253D%2528%2528A+new+look+at+Statistical+Model+Identification.%2529%2529%2526openedRefinements%253D*%2526sortType%253Ddesc_Publication+Year%2526matchBoolean%253Dtrue%2526rowsPerPage%253D50%2526searchField%253DSearch+All},
	Bdsk-Url-2 = {http://dx.doi.org/10.1109/TAC.1974.1100705}}

@book{MBishop:2006p485,
	Abstract = {The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the },
	Address = {Information Science and Statistics. NY, USA},
	Author = {Christopher M. Bishop},
	Date-Added = {2010-11-02 04:53:33 -0400},
	Date-Modified = {2013-06-27 16:41:15 +0000},
	Keywords = {Computers},
	Month = {Jan},
	Pages = {474--482},
	Pmid = {kTNoQgAACAAJ},
	Publisher = {Springer},
	Rating = {0},
	Title = {Pattern recognition and machine learning‎},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p485},
	Url = {http://books.google.com/books?id=kTNoQgAACAAJ&printsec=frontcover},
	Year = {2006},
	Bdsk-Url-1 = {http://books.google.com/books?id=kTNoQgAACAAJ&printsec=frontcover}}

@article{Chang:1983p484,
	Abstract = {SUMMARY In applying principal components for reducing the dimension of the data before clustering, it has ordinarily been the practice to use components with the largest eigenvalues. We prove, by means of a mixture of two multivariate normal distributions, },
	Author = {W Chang},
	Date-Added = {2010-11-02 03:00:24 -0400},
	Date-Modified = {2010-11-02 03:00:24 -0400},
	Journal = {Applied Statistics},
	Month = {Jan},
	Pmid = {8397809088665762531related:4zp4Zd8Ci3QJ},
	Rating = {0},
	Title = {On using principal components before separating a mixture of two multivariate normal distributions},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p484},
	Url = {http://www.jstor.org/stable/2347949},
	Year = {1983},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2347949}}

@book{Jolliffe:2010p479,
	Abstract = {Large datasets often include measurements on many variables. It may be possible to reduce the number of variables considerably while still retaining much of the information in the original dataset. A number of dimension-reducing techniques exist for doing this, and },
	Author = {Jolliffe, Ian},
	Date-Added = {2010-11-02 02:47:53 -0400},
	Date-Modified = {2010-11-02 09:45:47 -0400},
	Journal = {Wiley Online Library},
	Pmid = {13451665593502928837related:xQ8WWpbqrboJ},
	Publisher = {John Wiley \& Sons, Ltd},
	Rating = {0},
	Title = {Principal component analysis},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p479},
	Url = {http://onlinelibrary.wiley.com/doi/10.1002/0470013192.bsa501/full},
	Year = {1986},
	Bdsk-Url-1 = {http://onlinelibrary.wiley.com/doi/10.1002/0470013192.bsa501/full}}

@article{Ding:2003fk,
	Abstract = {MOTIVATION: Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Most current methods select genes based on known phenotype information. However, certain set of genes may correspond to new phenotypes which are yet unknown, and it is important to develop novel effective selection methods for their discovery without using any prior phenotype information. RESULTS: We propose and study a new method to select relevant genes based on their similarity information only. The method relies on a mechanism for discarding irrelevant genes. A two-way ordering of gene expression data can force irrelevant genes towards the middle in the ordering and thus can be discarded. Mechanisms based on variance and principal component analysis are also studied. When applied to expression profiles of colon cancer and leukemia, the unsupervised method outperforms the baseline algorithm that simply uses all genes, and it also selects relevant genes close to those selected using supervised methods. SUPPLEMENT: More results and software are online: http://www.nersc.gov/~cding/2way.},
	Author = {Ding, Chris H Q},
	Date-Added = {2010-11-02 02:37:10 -0400},
	Date-Modified = {2010-11-02 02:37:10 -0400},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Analysis of Variance; Cluster Analysis; Colonic Neoplasms; Gene Expression Profiling; Humans; Leukemia; Pattern Recognition, Automated; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Sequence Alignment; Sequence Analysis; Sequence Homology},
	Month = {Jul},
	Number = {10},
	Pages = {1259-66},
	Pmid = {12835270},
	Pst = {ppublish},
	Title = {Unsupervised feature selection via two-way ordering in gene expression analysis},
	Volume = {19},
	Year = {2003}}

@article{Dyrskjt:2004p476,
	Abstract = {The presence of carcinoma in situ (CIS) lesions in the urinary bladder is associated with a high risk of disease progression to a muscle invasive stage. In this study, we used microarray expression profiling to examine the gene expression patterns in superficial transitional cell carcinoma (sTCC) with surrounding CIS (13 patients), without surrounding CIS lesions (15 patients), and in muscle invasive carcinomas (mTCC; 13 patients). Hierarchical cluster analysis separated the sTCC samples according to the presence or absence of CIS in the surrounding urothelium. We identified a few gene clusters that contained genes with similar expression levels in transitional cell carcinoma (TCC) with surrounding CIS and invasive TCC. However, no close relationship between TCC with adjacent CIS and invasive TCC was observed using hierarchical cluster analysis. Expression profiling of a series of biopsies from normal urothelium and urothelium with CIS lesions from the same urinary bladder revealed that the gene expression found in sTCC with surrounding CIS is found also in CIS biopsies as well as in histologically normal samples adjacent to the CIS lesions. Furthermore, we also identified similar gene expression changes in mTCC samples. We used a supervised learning approach to build a 16-gene molecular CIS classifier. The classifier was able to classify sTCC samples according to the presence or absence of surrounding CIS with a high accuracy. This study demonstrates that a CIS gene expression signature is present not only in CIS biopsies but also in sTCC, mTCC, and, remarkably, in histologically normal urothelium from bladders with CIS. Identification of this expression signature could provide guidance for the selection of therapy and follow-up regimen in patients with early stage bladder cancer.},
	Affiliation = {Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, Aarhus N, Denmark.},
	Author = {Lars Dyrskj{\o}t and Mogens Kruh{\o}ffer and Thomas Thykjaer and Niels Marcussen and Jens L Jensen and Klaus M{\o}ller and Torben F {\O}rntoft},
	Date-Added = {2010-11-02 02:16:22 -0400},
	Date-Modified = {2010-11-02 02:16:23 -0400},
	Doi = {10.1158/0008-5472.CAN-03-3620},
	Journal = {Cancer Res},
	Keywords = {Humans, Carcinoma: Transitional Cell, Cluster Analysis, Biopsy, Neoplasm Staging, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Urinary Bladder Neoplasms, Carcinoma in Situ},
	Language = {eng},
	Month = {Jun},
	Number = {11},
	Pages = {4040--8},
	Pii = {64/11/4040},
	Pmid = {15173019},
	Rating = {0},
	Title = {Gene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p476},
	Volume = {64},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1158/0008-5472.CAN-03-3620}}

@article{Dyrskjt:2003p477,
	Abstract = {Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75\% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.},
	Affiliation = {Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Skejby, DK-8200 Aarhus N, Denmark.},
	Author = {Lars Dyrskj{\o}t and Thomas Thykjaer and Mogens Kruh{\o}ffer and Jens Ledet Jensen and Niels Marcussen and Stephen Hamilton-Dutoit and Hans Wolf and Torben F Orntoft},
	Date-Added = {2010-11-02 02:28:25 -0400},
	Date-Modified = {2010-11-02 02:28:25 -0400},
	Doi = {10.1038/ng1061},
	Journal = {Nat Genet},
	Keywords = {Neoplasm Staging, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, RNA: Messenger, Humans, Disease Progression, Genes: Neoplasm, Urinary Bladder Neoplasms, Immunohistochemistry, Gene Expression Regulation: Neoplastic, Prognosis, RNA: Neoplasm, Reproducibility of Results},
	Language = {eng},
	Month = {Jan},
	Number = {1},
	Pages = {90--6},
	Pii = {ng1061},
	Pmid = {12469123},
	Rating = {0},
	Title = {Identifying distinct classes of bladder carcinoma using microarrays},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p477},
	Volume = {33},
	Year = {2003},
	Bdsk-Url-1 = {http://dx.doi.org/10.1038/ng1061}}

@article{Yeung:2001p470,
	Abstract = {MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.},
	Affiliation = {Computer Science and Engineering, Box 352350, University of Washington, Seattle, WA 98195, USA. kayee@cs.washington.edu},
	Author = {K Y Yeung and W L Ruzzo},
	Date-Added = {2010-11-02 02:07:01 -0400},
	Date-Modified = {2010-12-19 18:37:48 -0500},
	Journal = {Bioinformatics},
	Keywords = {Genes: Neoplasm, Female, Cluster Analysis, Oligonucleotide Array Sequence Analysis, Algorithms, Genes: Fungal, Ovary, Humans, Gene Expression, Saccharomyces cerevisiae, Genes: cdc, Models: Statistical, Ovarian Neoplasms, Genes},
	Language = {eng},
	Month = {Sep},
	Number = {9},
	Pages = {763--74},
	Pmid = {11590094},
	Rating = {0},
	Title = {Principal component analysis for clustering gene expression data},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p470},
	Volume = {17},
	Year = {2001},
	Bdsk-File-1 = { }}

@article{Qu:2004p475,
	Abstract = {MOTIVATION: Grouping genes having similar expression patterns is called gene clustering, which has been proved to be a useful tool for extracting underlying biological information of gene expression data. Many clustering procedures have shown success in microarray gene clustering; most of them belong to the family of heuristic clustering algorithms. Model-based algorithms are alternative clustering algorithms, which are based on the assumption that the whole set of microarray data is a finite mixture of a certain type of distributions with different parameters. Application of the model-based algorithms to unsupervised clustering has been reported. Here, for the first time, we demonstrated the use of the model-based algorithm in supervised clustering of microarray data. RESULTS: We applied the proposed methods to real gene expression data and simulated data. We showed that the supervised model-based algorithm is superior over the unsupervised method and the support vector machines (SVM) method. AVAILABILITY: The program written in the SAS language implementing methods I-III in this report is available upon request. The software of SVMs is available in the website http://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi},
	Affiliation = {Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.},
	Author = {Yi Qu and Shizhong Xu},
	Date-Added = {2010-11-02 02:13:39 -0400},
	Date-Modified = {2010-12-19 18:37:09 -0500},
	Doi = {10.1093/bioinformatics/bth177},
	Journal = {Bioinformatics},
	Keywords = {Models: Genetic, Models: Statistical, Oligonucleotide Array Sequence Analysis, Cell Cycle Proteins, Cluster Analysis, Gene Expression Profiling, Algorithms, Sequence Analysis: DNA, Software, Sequence Alignment, Saccharomyces cerevisiae Proteins, Normal Distribution, Artificial Intelligence, Multivariate Analysis},
	Language = {eng},
	Month = {Aug},
	Number = {12},
	Pages = {1905--13},
	Pii = {bth177},
	Pmid = {15044244},
	Rating = {0},
	Title = {Supervised cluster analysis for microarray data based on multivariate Gaussian mixture},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p475},
	Volume = {20},
	Year = {2004},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bth177}}

@article{Ma:2009p467,
	Abstract = {MOTIVATION: Development of high-throughput technology makes it possible to measure expressions of thousands of genes simultaneously. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated biological functions. It is of great interest to identify differential gene pathways that are associated with the variations of phenotypes. RESULTS: We propose the following approach for detecting differential gene pathways. First, we construct gene pathways using databases such as KEGG or GO. Second, for each pathway, we extract a small number of representative features, which are linear combinations of gene expressions and/or their transformations. Specifically, we propose using (i) principal components (PCs) of gene expression sets, (ii) PCs of expanded gene expression sets and (iii) expanded sets of PCs of gene expressions, as the representative features. Third, we identify differential gene pathways as those with representative features significantly associated with the variations of phenotypes, particularly disease clinical outcomes, in regression models. The false discovery rate approach is used to adjust for multiple comparisons. Analysis of three gene expression datasets suggests that (i) the proposed approach can effectively identify differential gene pathways; (ii) PCs that explain only a small amount of variations of gene expressions may bear significant associations between gene pathways and phenotypes; (iii) including second-order terms of gene expressions may lead to identification of new differential gene pathways; (iv) the proposed approach is relatively insensitive to additional noises; and (v) the proposed approach can identify gene pathways missed by alternative approaches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	Affiliation = {Department of Epidemiology and Public Health, Yale University, New Haven, CT 06510, USA. shuangge.ma@yale.edu},
	Author = {Shuangge Ma and Michael R Kosorok},
	Date-Added = {2010-11-02 02:07:02 -0400},
	Date-Modified = {2010-12-19 18:38:24 -0500},
	Doi = {10.1093/bioinformatics/btp085},
	Journal = {Bioinformatics},
	Keywords = {Gene Expression Profiling, Databases: Genetic, Principal Component Analysis, Computational Biology, Phenotype},
	Language = {eng},
	Month = {Apr},
	Number = {7},
	Pages = {882--9},
	Pii = {btp085},
	Pmid = {19223452},
	Rating = {0},
	Title = {Identification of differential gene pathways with principal component analysis},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p467},
	Volume = {25},
	Year = {2009},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btp085}}

@article{Medvedovic:2004p473,
	Abstract = {MOTIVATION: Identifying patterns of co-expression in microarray data by cluster analysis has been a productive approach to uncovering molecular mechanisms underlying biological processes under investigation. Using experimental replicates can generally improve the precision of the cluster analysis by reducing the experimental variability of measurements. In such situations, Bayesian mixtures allow for an efficient use of information by precisely modeling between-replicates variability. RESULTS: We developed different variants of Bayesian mixture based clustering procedures for clustering gene expression data with experimental replicates. In this approach, the statistical distribution of microarray data is described by a Bayesian mixture model. Clusters of co-expressed genes are created from the posterior distribution of clusterings, which is estimated by a Gibbs sampler. We define infinite and finite Bayesian mixture models with different between-replicates variance structures and investigate their utility by analyzing synthetic and the real-world datasets. Results of our analyses demonstrate that (1) improvements in precision achieved by performing only two experimental replicates can be dramatic when the between-replicates variability is high, (2) precise modeling of intra-gene variability is important for accurate identification of co-expressed genes and (3) the infinite mixture model with the 'elliptical' between-replicates variance structure performed overall better than any other method tested. We also introduce a heuristic modification to the Gibbs sampler based on the 'reverse annealing' principle. This modification effectively overcomes the tendency of the Gibbs sampler to converge to different modes of the posterior distribution when started from different initial positions. Finally, we demonstrate that the Bayesian infinite mixture model with 'elliptical' variance structure is capable of identifying the underlying structure of the data without knowing the 'correct' number of clusters. AVAILABILITY: The MS Windows based program named Gaussian Infinite Mixture Modeling (GIMM) implementing the Gibbs sampler and corresponding C++ code are available at http://homepages.uc.edu/~medvedm/GIMM.htm SUPPLEMENTAL INFORMATION: http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic_bioinf2003.html},
	Affiliation = {Department of Environmental Health, Center for Genome Information, University of Cincinnati Medical Center, 3223 Eden Avenue ML 56, Cincinnati, OH 45267-0056, USA. Mario.Medvedovic@uc.edu},
	Author = {M Medvedovic and K Y Yeung and R E Bumgarner},
	Date-Added = {2010-11-02 02:13:40 -0400},
	Date-Modified = {2010-12-19 18:38:40 -0500},
	Doi = {10.1093/bioinformatics/bth068},
	Journal = {Bioinformatics},
	Keywords = {Models: Genetic, Sensitivity and Specificity, Sequence Analysis: DNA, Sample Size, Sequence Alignment, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Computer Simulation, Cluster Analysis, Genetic Variation, Reproducibility of Results, Models: Statistical, Bayes Theorem, Algorithms},
	Language = {eng},
	Month = {May},
	Number = {8},
	Pages = {1222--32},
	Pii = {bth068},
	Pmid = {14871871},
	Rating = {0},
	Title = {Bayesian mixture model based clustering of replicated microarray data},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p473},
	Volume = {20},
	Year = {2004},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bth068}}

@article{McNicholas:2010p280,
	Abstract = {MOTIVATION: In recent years, work has been carried out on clustering gene expression microarray data. Some approaches are developed from an algorithmic viewpoint whereas others are developed via the application of mixture models. In this article, a family of eight mixture models which utilizes the factor analysis covariance structure is extended to 12 models and applied to gene expression microarray data. This modelling approach builds on previous work by introducing a modified factor analysis covariance structure, leading to a family of 12 mixture models, including parsimonious models. This family of models allows for the modelling of the correlation between gene expression levels even when the number of samples is small. Parameter estimation is carried out using a variant of the expectation-maximization algorithm and model selection is achieved using the Bayesian information criterion. This expanded family of Gaussian mixture models, known as the expanded parsimonious Gaussian mixture model (EPGMM) family, is then applied to two well-known gene expression data sets. RESULTS: The performance of the EPGMM family of models is quantified using the adjusted Rand index. This family of models gives very good performance, relative to existing popular clustering techniques, when applied to real gene expression microarray data. AVAILABILITY: The reduced, preprocessed data that were analysed are available at www.paulmcnicholas.info CONTACT: pmcnicho@uoguelph.ca.},
	Affiliation = {Department of Mathematics {\&} Statistics, University of Guelph, Guelph, ON, Canada, N1G2W1 and School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland.},
	Author = {Paul D McNicholas and Thomas Brendan Murphy},
	Date-Added = {2010-10-26 00:26:40 -0400},
	Date-Modified = {2010-12-19 18:38:03 -0500},
	Doi = {10.1093/bioinformatics/btq498},
	Journal = {Bioinformatics},
	Language = {eng},
	Month = {Nov},
	Number = {21},
	Pages = {2705--12},
	Pii = {btq498},
	Pmid = {20802251},
	Rating = {0},
	Read = {Yes},
	Title = {Model-based clustering of microarray expression data via latent Gaussian mixture models},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p280},
	Volume = {26},
	Year = {2010},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btq498}}

@article{Tipping:1999p418,
	Abstract = {Summary. Principal  (PCA) is a ubiquitous technique for data  and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum likelihood },
	Author = {M Tipping{\ldots}},
	Date-Added = {2010-11-02 01:35:35 -0400},
	Date-Modified = {2010-11-02 01:35:35 -0400},
	Journal = {Journal of the Royal Statistical {\ldots}},
	Month = {Jan},
	Pmid = {16501393779809560709related:hahj8Da6AOUJ},
	Rating = {0},
	Title = {Probabilistic principal component analysis},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p418},
	Url = {http://www3.interscience.wiley.com/journal/119099509/abstract},
	Year = {1999},
	Bdsk-Url-1 = {http://www3.interscience.wiley.com/journal/119099509/abstract}}

@article{Ouyang:2004p474,
	Abstract = {MOTIVATION: In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90\% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. RESULTS: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.},
	Affiliation = {Environmental and Occupational Health Sciences Institute, UMDNJ-Robert Wood Johnson Medical School and Rutgers, The State University of New Jersey, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA. ouyang@fidelio.rutgers.edu},
	Author = {Ming Ouyang and William J Welsh and Panos Georgopoulos},
	Date-Added = {2010-11-02 02:13:37 -0400},
	Date-Modified = {2010-12-19 18:38:35 -0500},
	Doi = {10.1093/bioinformatics/bth007},
	Journal = {Bioinformatics},
	Keywords = {Models: Genetic, Cluster Analysis, Reproducibility of Results, Cell Cycle, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Sensitivity and Specificity, Artifacts, Normal Distribution, Yeasts, Sequence Analysis: DNA, Benchmarking, Models: Statistical, Algorithms},
	Language = {eng},
	Month = {Apr},
	Number = {6},
	Pages = {917--23},
	Pii = {bth007},
	Pmid = {14751970},
	Rating = {0},
	Title = {Gaussian mixture clustering and imputation of microarray data},
	Uri = {papers://AB627ADF-989B-4D45-86E2-76956839AE29/Paper/p474},
	Volume = {20},
	Year = {2004},
	Bdsk-File-1 = { },
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bth007}}
