K
Kevin K. Dobbin
Researcher at University of Georgia
Publications - 67
Citations - 6422
Kevin K. Dobbin is an academic researcher from University of Georgia. The author has contributed to research in topics: Sample size determination & Medicine. The author has an hindex of 25, co-authored 62 publications receiving 5973 citations. Previous affiliations of Kevin K. Dobbin include National Institutes of Health.
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Journal ArticleDOI
Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.
Kerby Shedden,Jeremy M. G. Taylor,Steven A. Enkemann,Ming-Sound Tsao,Timothy J. Yeatman,William L. Gerald,Steven A. Eschrich,Igor Jurisica,Thomas J. Giordano,David E. Misek,Andrew C. Chang,Chang-Qi Zhu,Daniel Strumpf,Samir M. Hanash,Frances A. Shepherd,Keyue Ding,Lesley Seymour,Katsuhiko Naoki,Nathan A. Pennell,Barbara A. Weir,Roeland Verhaak,Christine Ladd-Acosta,Todd R. Golub,Michael Gruidl,Anupama Sharma,Janos Szoke,Maureen F. Zakowski,Valerie W. Rusch,Mark G. Kris,Agnes Viale,Noriko Motoi,William D. Travis,Barbara A. Conley,Venkatraman E. Seshan,Matthew Meyerson,Matthew Meyerson,Rork Kuick,Kevin K. Dobbin,Tracy Lively,James W. Jacobson,David G. Beer +40 more
TL;DR: A large, training–testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas, providing the largest available set of microarray data with extensive pathological and clinical annotation for lungAdenocARCinomas.
Journal ArticleDOI
Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification
TL;DR: In this article, the authors address statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called "class prediction", which includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
COMMENTARY Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification
TL;DR: Statistical issues that arise from the use of DNA microarrays for an important group of objectives that has been called “class prediction” are addressed, which includes derivation of predictors of prognosis, response to therapy, or any phenotype or genotype defined independently of the gene expression profile.
Journal ArticleDOI
Rearrangement of CRLF2 is associated with mutation of JAK kinases, alteration of IKZF1, Hispanic/Latino ethnicity, and a poor outcome in pediatric B-progenitor acute lymphoblastic leukemia
Richard C. Harvey,Richard C. Harvey,Charles G. Mullighan,I-Ming Chen,I-Ming Chen,Walker Wharton,Fady M. Mikhail,Andrew J. Carroll,Andrew J. Carroll,Huining Kang,Wei Liu,Kevin K. Dobbin,Malcolm A. Smith,William L. Carroll,William L. Carroll,Meenakshi Devidas,Meenakshi Devidas,W. Paul Bowman,Bruce M. Camitta,Bruce M. Camitta,Gregory H. Reaman,Gregory H. Reaman,Stephen P. Hunger,Stephen P. Hunger,James R. Downing,Cheryl L. Willman,Cheryl L. Willman +26 more
TL;DR: Observations suggest that activation of CRLF2 expression, mutation of JAK kinases, and alterations of IKZF1 cooperate to promote B-cell leukemogenesis and identify these pathways as important therapeutic targets in this disease.
Journal ArticleDOI
Identification of novel cluster groups in pediatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome.
Richard C. Harvey,Richard C. Harvey,Charles G. Mullighan,Xuefei Wang,Kevin K. Dobbin,George S. Davidson,Edward J. Bedrick,I-Ming Chen,I-Ming Chen,Susan R. Atlas,Huining Kang,Kerem Ar,Carla S. Wilson,Walker Wharton,Maurice H. Murphy,Meenakshi Devidas,Meenakshi Devidas,Andrew J. Carroll,Andrew J. Carroll,Michael J. Borowitz,Michael J. Borowitz,W. Paul Bowman,James R. Downing,Mary V. Relling,Jun J. Yang,Deepa Bhojwani,William L. Carroll,William L. Carroll,Bruce M. Camitta,Bruce M. Camitta,Gregory H. Reaman,Gregory H. Reaman,Malcolm A. Smith,Stephen P. Hunger,Stephen P. Hunger,Cheryl L. Willman,Cheryl L. Willman +36 more
TL;DR: Striking clinical and genetic heterogeneity in high-risk ALL is revealed and novel genes that may serve as new targets for diagnosis, risk classification, and therapy are pointed to.