@article {20676074,
	title = {The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.},
	journal = {Nature biotechnology},
	volume = {28},
	year = {2010},
	month = {2010 Aug},
	pages = {827-38},
	abstract = {<p>Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, \&gt;30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.</p>},
	url = {http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html},
	author = {Shi, Leming and Campbell, Gregory and Jones, Wendell D and Campagne, Fabien and Wen, Zhining and Walker, Stephen J and Su, Zhenqiang and Chu, Tzu-Ming and Goodsaid, Federico M and Pusztai, Lajos and Shaughnessy, John D and Oberthuer, Andr{\'e} and Thomas, Russell S and Paules, Richard S and Fielden, Mark and Barlogie, Bart and Chen, Weijie and Du, Pan and Fischer, Matthias and Furlanello, Cesare and Gallas, Brandon D and Ge, Xijin and Megherbi, Dalila B and Symmans, W Fraser and Wang, May D and Zhang, John and Bitter, Hans and Brors, Benedikt and Bushel, Pierre R and Bylesjo, Max and Chen, Minjun and Cheng, Jie and Cheng, Jing and Chou, Jeff and Davison, Timothy S and Delorenzi, Mauro and Deng, Youping and Devanarayan, Viswanath and Dix, David J and Dopazo, Joaquin and Dorff, Kevin C and Elloumi, Fathi and Fan, Jianqing and Fan, Shicai and Fan, Xiaohui and Fang, Hong and Gonzaludo, Nina and Hess, Kenneth R and Hong, Huixiao and Huan, Jun and Irizarry, Rafael A and Judson, Richard and Juraeva, Dilafruz and Lababidi, Samir and Lambert, Christophe G and Li, Li and Li, Yanen and Li, Zhen and Lin, Simon M and Liu, Guozhen and Lobenhofer, Edward K and Luo, Jun and Luo, Wen and McCall, Matthew N and Nikolsky, Yuri and Pennello, Gene A and Perkins, Roger G and Philip, Reena and Popovici, Vlad and Price, Nathan D and Qian, Feng and Scherer, Andreas and Shi, Tieliu and Shi, Weiwei and Sung, Jaeyun and Thierry-Mieg, Danielle and Thierry-Mieg, Jean and Thodima, Venkata and Trygg, Johan and Vishnuvajjala, Lakshmi and Wang, Sue Jane and Wu, Jianping and Wu, Yichao and Xie, Qian and Yousef, Waleed A and Zhang, Liang and Zhang, Xuegong and Zhong, Sheng and Zhou, Yiming and Zhu, Sheng and Arasappan, Dhivya and Bao, Wenjun and Lucas, Anne Bergstrom and Berthold, Frank and Brennan, Richard J and Buness, Andreas and Catalano, Jennifer G and Chang, Chang and Chen, Rong and Cheng, Yiyu and Cui, Jian and Czika, Wendy and Demichelis, Francesca and Deng, Xutao and Dosymbekov, Damir and Eils, Roland and Feng, Yang and Fostel, Jennifer and Fulmer-Smentek, Stephanie and Fuscoe, James C and Gatto, Laurent and Ge, Weigong and Goldstein, Darlene R and Guo, Li and Halbert, Donald N and Han, Jing and Harris, Stephen C and Hatzis, Christos and Herman, Damir and Huang, Jianping and Jensen, Roderick V and Jiang, Rui and Johnson, Charles D and Jurman, Giuseppe and Kahlert, Yvonne and Khuder, Sadik A and Kohl, Matthias and Li, Jianying and Li, Li and Li, Menglong and Li, Quan-Zhen and Li, Shao and Li, Zhiguang and Liu, Jie and Liu, Ying and Liu, Zhichao and Meng, Lu and Madera, Manuel and Martinez-Murillo, Francisco and Medina, Ignacio and Meehan, Joseph and Miclaus, Kelci and Moffitt, Richard A and Montaner, David and Mukherjee, Piali and Mulligan, George J and Neville, Padraic and Nikolskaya, Tatiana and Ning, Baitang and Page, Grier P and Parker, Joel and Parry, R Mitchell and Peng, Xuejun and Peterson, Ron L and Phan, John H and Quanz, Brian and Ren, Yi and Riccadonna, Samantha and Roter, Alan H and Samuelson, Frank W and Schumacher, Martin M and Shambaugh, Joseph D and Shi, Qiang and Shippy, Richard and Si, Shengzhu and Smalter, Aaron and Sotiriou, Christos and Soukup, Mat and Staedtler, Frank and Steiner, Guido and Stokes, Todd H and Sun, Qinglan and Tan, Pei-Yi and Tang, Rong and Tezak, Zivana and Thorn, Brett and Tsyganova, Marina and Turpaz, Yaron and Vega, Silvia C and Visintainer, Roberto and von Frese, Juergen and Wang, Charles and Wang, Eric and Wang, Junwei and Wang, Wei and Westermann, Frank and Willey, James C and Woods, Matthew and Wu, Shujian and Xiao, Nianqing and Xu, Joshua and Xu, Lei and Yang, Lun and Zeng, Xiao and Zhang, Jialu and Zhang, Li and Zhang, Min and Zhao, Chen and Puri, Raj K and Scherf, Uwe and Tong, Weida and Wolfinger, Russell D}
}
@article {574,
	title = {Multidimensional gene set analysis of genomic data.},
	journal = {PLoS One},
	volume = {5},
	year = {2010},
	month = {2010 Apr 27},
	pages = {e10348},
	abstract = {<p>Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.</p>},
	keywords = {Databases, Genetic, Gene Expression Profiling, Gene Regulatory Networks, Genome, Human, Genomics, Humans, Models, Statistical},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0010348},
	author = {Montaner, David and Dopazo, Joaquin}
}
