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    • Comparison of RNA-seq and microarray-based models for clinical endpoint prediction 

      Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D.; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H.; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G.; He, Min Max; Hebbring, Scott; Kaufman, Howard L.; Lababidi, Samir; Lancashire, Lee J.; Li, Yan; Lu, Xin X.; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H.; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K.; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D.; Shi, Tieliu; Malkas, Linda H.; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias (BioMed Central, 2015)
      Background: Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond ...