B
Baitang Ning
Researcher at Food and Drug Administration
Publications - 34
Citations - 3985
Baitang Ning is an academic researcher from Food and Drug Administration. The author has contributed to research in topics: microRNA & Biology. The author has an hindex of 16, co-authored 29 publications receiving 3641 citations. Previous affiliations of Baitang Ning include National Center for Toxicological Research & United States Department of Health and Human Services.
Papers
More filters
Journal ArticleDOI
The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements
Leming Shi,Laura H. Reid,Wendell D. Jones,Richard Shippy,Janet A. Warrington,Shawn C. Baker,Patrick J. Collins,Francoise de Longueville,Ernest S. Kawasaki,Kathleen Y. Lee,Yuling Luo,Yongming Andrew Sun,James C. Willey,Robert Setterquist,Gavin M. Fischer,Weida Tong,Yvonne P. Dragan,David J. Dix,Felix W. Frueh,Federico Goodsaid,Damir Herman,Roderick V. Jensen,Charles D. Johnson,Edward K. Lobenhofer,Raj K. Puri,Uwe Scherf,Jean Thierry-Mieg,Charles Wang,Michael A Wilson,Paul K. Wolber,Lu Zhang,William Slikker,Shashi Amur,Wenjun Bao,Catalin Barbacioru,Anne Bergstrom Lucas,Vincent Bertholet,Cecilie Boysen,Bud Bromley,Donna Brown,Alan Brunner,Roger D. Canales,Xiaoxi Megan Cao,Thomas A. Cebula,James J. Chen,Jing Cheng,Tzu Ming Chu,Eugene Chudin,John F. Corson,J. Christopher Corton,Lisa J. Croner,Christopher Davies,Timothy Davison,Glenda C. Delenstarr,Xutao Deng,David Dorris,Aron Charles Eklund,Xiaohui Fan,Hong Fang,Stephanie Fulmer-Smentek,James C. Fuscoe,Kathryn Gallagher,Weigong Ge,Lei Guo,Xu Guo,Janet Hager,Paul K. Haje,Jing Han,Tao Han,Heather Harbottle,Stephen C. Harris,Eli Hatchwell,Craig A. Hauser,Susan D. Hester,Huixiao Hong,Patrick Hurban,Scott A. Jackson,Hanlee P. Ji,Charles R. Knight,Winston Patrick Kuo,J. Eugene LeClerc,Shawn Levy,Quan Zhen Li,Chunmei Liu,Ying Liu,Michael Lombardi,Yunqing Ma,Scott R. Magnuson,Botoul Maqsodi,Timothy K. McDaniel,Nan Mei,Ola Myklebost,Baitang Ning,Natalia Novoradovskaya,Michael S. Orr,Terry Osborn,Adam Papallo,Tucker A. Patterson,Roger Perkins,Elizabeth Herness Peters,Ron L. Peterson,Kenneth L. Philips,P. Scott Pine,Lajos Pusztai,Feng Qian,Hongzu Ren,Mitch Rosen,Barry A. Rosenzweig,Raymond R. Samaha,Mark Schena,Gary P. Schroth,Svetlana Shchegrova,Dave D. Smith,Frank Staedtler,Zhenqiang Su,Hongmei Sun,Zoltan Szallasi,Zivana Tezak,Danielle Thierry-Mieg,Karol L. Thompson,Irina Tikhonova,Yaron Turpaz,Beena Vallanat,Christophe Van,Stephen J. Walker,Sue Jane Wang,Yonghong Wang,Russell D. Wolfinger,Alexander Wong,Jie Wu,Chunlin Xiao,Qian Xie,Jun Xu,Wen Yang,Liang Zhang,Sheng Zhong,Yaping Zong +136 more
TL;DR: This study describes the experimental design and probe mapping efforts behind the MicroArray Quality Control project and shows intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed.
Journal ArticleDOI
The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Leming Shi,Gregory Campbell,Wendell D. Jones,Fabien Campagne,Zhining Wen,Stephen J. Walker,Zhenqiang Su,Tzu Ming Chu,Federico Goodsaid,Lajos Pusztai,John D. Shaughnessy,André Oberthuer,Russell S. Thomas,Richard S. Paules,Mark R. 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 Bylesjö,Minjun Chen,Jie Cheng,Jing Cheng,Jeff W. Chou,Timothy Davison,Mauro Delorenzi,Youping Deng,Viswanath Devanarayan,David J. Dix,Joaquín 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 S. Judson,Dilafruz Juraeva,Samir Lababidi,Christophe G. Lambert,Li Li,Yanen Li,Zhen Li,Simon Lin,Guozhen Liu,Edward K. Lobenhofer,J. Luo,Wen Luo,Matthew N. McCall,Yuri Nikolsky,Gene Pennello,Roger 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 Lee,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,K. Miclaus,Richard A. Moffitt,David Montaner,Piali Mukherjee,George Mulligan,Padraic Neville,Tatiana Nikolskaya,Baitang Ning,Grier P. Page,Joel S. 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 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 T. Thorn,Marina Tsyganova,Yaron Turpaz,S. 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 Zheng,Min Zhang,Chen Zhao,Raj K. Puri,Uwe Scherf,Weida Tong,Russell D. Wolfinger +201 more
TL;DR: P 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 are generated.
Journal ArticleDOI
Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.
Wenqian Zhang,Ying Yu,Falk Hertwig,Falk Hertwig,Jean Thierry-Mieg,Wenwei Zhang,Danielle Thierry-Mieg,Jian Wang,Cesare Furlanello,Viswanath Devanarayan,Jie Cheng,Youping Deng,Barbara Hero,Huixiao Hong,Meiwen Jia,Li Li,Simon Lin,Yuri Nikolsky,André Oberthuer,Tao Qing,Zhenqiang Su,Ruth Volland,Charles Wang,May D. Wang,Junmei Ai,Davide Albanese,Shahab Asgharzadeh,Smadar Avigad,Wenjun Bao,Marina Bessarabova,Murray H. Brilliant,Benedikt Brors,Marco Chierici,Tzu-Ming Chu,Jibin Zhang,Richard Grundy,Min Max He,Scott J. Hebbring,Howard L. Kaufman,Samir Lababidi,Lee Lancashire,Yan Li,Xin X. Lu,Heng Luo,Heng Luo,Xiwen Ma,Baitang Ning,Rosa Noguera,Martin Peifer,John H. Phan,Frederik Roels,Frederik Roels,Carolina Rosswog,Susan Shao,Jie Shen,Jessica Theissen,Gian Paolo Tonini,Jo Vandesompele,Po-Yen Wu,Wenzhong Xiao,Joshua Xu,Weihong Xu,Jiekun Xuan,Yong Yang,Zhan Ye,Zirui Dong,Ke Zhang,Ye Yin,Chen Zhao,Yuanting Zheng,Russell D. Wolfinger,Tieliu Shi,Linda H. Malkas,Frank Berthold,Frank Berthold,Jun Wang,Weida Tong,Leming Shi,Leming Shi,Zhiyu Peng,Matthias Fischer,Matthias Fischer +81 more
TL;DR: It is demonstrated thatRNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction.
Journal ArticleDOI
Similarities and Differences in the Expression of Drug-Metabolizing Enzymes between Human Hepatic Cell Lines and Primary Human Hepatocytes
Lei Guo,Stacey L. Dial,Leming Shi,William S. Branham,Jie Liu,Jia-Long Fang,Bridgett Green,Helen Deng,Jim Kaput,Baitang Ning +9 more
TL;DR: The basal gene expression profiles of 251 drug-metabolizing enzymes in untreated primary human hepatocytes from six donors, four commonly used hepatoma cell lines, and one transfected human liver epithelial cell line are characterized, providing references for researchers to choose carefully appropriate in vitro models for studies of drug metabolism and toxicity.
Journal ArticleDOI
Comparing next-generation sequencing and microarray technologies in a toxicological study of the effects of aristolochic acid on rat kidneys.
Zhenqiang Su,Zhiguang Li,Tao Chen,Quan Zhen Li,Hong Fang,Don Ding,Weigong Ge,Baitang Ning,Huixiao Hong,Roger Perkins,Weida Tong,Leming Shi +11 more
TL;DR: It is found that RNA-Seq was more sensitive in detecting genes with low expression levels, while similar gene expression patterns were observed for both platforms, which helped to maintain a consistent biological interpretation with time-tested microarray platforms.