UNLABELLED: High-throughput data can be used in conjunction with clinical information to develop predictive models. Automating the process of developing, evaluating and testing such predictive models on different datasets would minimize operator errors and facilitate the comparison of different modeling approaches on the same dataset. Complete automation would also yield unambiguous documentation of the process followed to develop each model. We present the BDVal suite of programs that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process. We have used BDVal to construct models from microarray and proteomics data, as well as from DNA-methylation datasets. The programs are designed for scalability and support the construction of thousands of alternative models from a given dataset and prediction task. AVAILABILITY AND IMPLEMENTATION: The BDVal programs are implemented in Java, provided under the GNU General Public License and freely available at http://bdval.campagnelab.org.
UNLABELLED: High-throughput data can be used in conjunction with clinical information to develop predictive models. Automating the process of developing, evaluating and testing such predictive models on different datasets would minimize operator errors and facilitate the comparison of different modeling approaches on the same dataset. Complete automation would also yield unambiguous documentation of the process followed to develop each model. We present the BDVal suite of programs that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process. We have used BDVal to construct models from microarray and proteomics data, as well as from DNA-methylation datasets. The programs are designed for scalability and support the construction of thousands of alternative models from a given dataset and prediction task. AVAILABILITY AND IMPLEMENTATION: The BDVal programs are implemented in Java, provided under the GNU General Public License and freely available at http://bdval.campagnelab.org.
Authors: Leming Shi; Gregory Campbell; Wendell D Jones; Fabien Campagne; Zhining Wen; Stephen J Walker; Zhenqiang Su; Tzu-Ming Chu; Federico M Goodsaid; Lajos Pusztai; John D Shaughnessy; André Oberthuer; Russell S Thomas; Richard S Paules; Mark Fielden; Bart Barlogie; Weijie Chen; Pan Du; Matthias Fischer; Cesare Furlanello; Brandon D Gallas; Xijin Ge; Dalila B Megherbi; W Fraser Symmans; May D Wang; John Zhang; Hans Bitter; Benedikt Brors; Pierre R Bushel; Max Bylesjo; Minjun Chen; Jie Cheng; Jing Cheng; Jeff Chou; Timothy S Davison; Mauro Delorenzi; Youping Deng; Viswanath Devanarayan; David J Dix; Joaquin Dopazo; Kevin C Dorff; Fathi Elloumi; Jianqing Fan; Shicai Fan; Xiaohui Fan; Hong Fang; Nina Gonzaludo; Kenneth R Hess; Huixiao Hong; Jun Huan; Rafael A Irizarry; Richard Judson; Dilafruz Juraeva; Samir Lababidi; Christophe G Lambert; Li Li; Yanen Li; Zhen Li; Simon M Lin; Guozhen Liu; Edward K Lobenhofer; Jun Luo; Wen Luo; Matthew N McCall; Yuri Nikolsky; Gene A Pennello; Roger G Perkins; Reena Philip; Vlad Popovici; Nathan D Price; Feng Qian; Andreas Scherer; Tieliu Shi; Weiwei Shi; Jaeyun Sung; Danielle Thierry-Mieg; Jean Thierry-Mieg; Venkata Thodima; Johan Trygg; Lakshmi Vishnuvajjala; Sue Jane Wang; Jianping Wu; Yichao Wu; Qian Xie; Waleed A Yousef; Liang Zhang; Xuegong Zhang; Sheng Zhong; Yiming Zhou; Sheng Zhu; Dhivya Arasappan; Wenjun Bao; Anne Bergstrom Lucas; Frank Berthold; Richard J Brennan; Andreas Buness; Jennifer G Catalano; Chang Chang; Rong Chen; Yiyu Cheng; Jian Cui; Wendy Czika; Francesca Demichelis; Xutao Deng; Damir Dosymbekov; Roland Eils; Yang Feng; Jennifer Fostel; Stephanie Fulmer-Smentek; James C Fuscoe; Laurent Gatto; Weigong Ge; Darlene R Goldstein; Li Guo; Donald N Halbert; Jing Han; Stephen C Harris; Christos Hatzis; Damir Herman; Jianping Huang; Roderick V Jensen; Rui Jiang; Charles D Johnson; Giuseppe Jurman; Yvonne Kahlert; Sadik A Khuder; Matthias Kohl; Jianying Li; Li Li; Menglong Li; Quan-Zhen Li; Shao Li; Zhiguang Li; Jie Liu; Ying Liu; Zhichao Liu; Lu Meng; Manuel Madera; Francisco Martinez-Murillo; Ignacio Medina; Joseph Meehan; Kelci Miclaus; Richard A Moffitt; David Montaner; Piali Mukherjee; George J Mulligan; Padraic Neville; Tatiana Nikolskaya; Baitang Ning; Grier P Page; Joel Parker; R Mitchell Parry; Xuejun Peng; Ron L Peterson; John H Phan; Brian Quanz; Yi Ren; Samantha Riccadonna; Alan H Roter; Frank W Samuelson; Martin M Schumacher; Joseph D Shambaugh; Qiang Shi; Richard Shippy; Shengzhu Si; Aaron Smalter; Christos Sotiriou; Mat Soukup; Frank Staedtler; Guido Steiner; Todd H Stokes; Qinglan Sun; Pei-Yi Tan; Rong Tang; Zivana Tezak; Brett Thorn; Marina Tsyganova; Yaron Turpaz; Silvia C Vega; Roberto Visintainer; Juergen von Frese; Charles Wang; Eric Wang; Junwei Wang; Wei Wang; Frank Westermann; James C Willey; Matthew Woods; Shujian Wu; Nianqing Xiao; Joshua Xu; Lei Xu; Lun Yang; Xiao Zeng; Jialu Zhang; Li Zhang; Min Zhang; Chen Zhao; Raj K Puri; Uwe Scherf; Weida Tong; Russell D Wolfinger Journal: Nat Biotechnol Date: 2010-07-30 Impact factor: 54.908