M
Mario Medvedovic
Researcher at University of Cincinnati
Publications - 193
Citations - 8560
Mario Medvedovic is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Gene expression & Gene expression profiling. The author has an hindex of 44, co-authored 177 publications receiving 7239 citations. Previous affiliations of Mario Medvedovic include University of Michigan & University of Miami.
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Journal ArticleDOI
Proximity of Chromosomal Loci That Participate in Radiation-Induced Rearrangements in Human Cells
Marina N. Nikiforova,James R. Stringer,Ruthann I. Blough,Mario Medvedovic,James A. Fagin,Yuri E. Nikiforov +5 more
TL;DR: Spatial contiguity of RET and H4 may provide a structural basis for generation of RET/PTC1 rearrangement by allowing a single radiation track to produce a double-strand break in each gene at the same site in the nucleus.
Journal ArticleDOI
Bayesian infinite mixture model based clustering of gene expression profiles.
TL;DR: A clustering procedure based on the Bayesian infinite mixture model and applied to clustering gene expression profiles that allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles.
Journal ArticleDOI
The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations
Alexandra B Keenan,Sherry L. Jenkins,Kathleen M. Jagodnik,Simon Koplev,Edward He,Denis Torre,Zichen Wang,Anders B. Dohlman,Moshe C. Silverstein,Alexander Lachmann,Maxim V. Kuleshov,Avi Ma'ayan,Vasileios Stathias,Raymond Terryn,Daniel J. Cooper,Michele Forlin,Amar Koleti,Dusica Vidovic,Caty Chung,Stephan C. Schürer,Jouzas Vasiliauskas,Marcin Pilarczyk,Behrouz Shamsaei,Mehdi Fazel,Yan Ren,Wen Niu,Nicholas A. Clark,Shana White,Naim Al Mahi,Lixia Zhang,Michal Kouril,John F. Reichard,Siva Sivaganesan,Mario Medvedovic,Jaroslaw Meller,Rick J. Koch,Marc R. Birtwistle,Ravi Iyengar,Eric A. Sobie,Evren U. Azeloglu,Julia A. Kaye,Jeannette Osterloh,Kelly Haston,Jaslin Kalra,Steve Finkbiener,Jonathan Z. Li,Pamela Milani,Miriam Adam,Renan Escalante-Chong,Karen Sachs,Alexander LeNail,Divya Ramamoorthy,Ernest Fraenkel,Gavin Daigle,Uzma Hussain,Alyssa Coye,Jeffrey D. Rothstein,Dhruv Sareen,Loren Ornelas,Maria G. Banuelos,Berhan Mandefro,Ritchie Ho,Clive N. Svendsen,Ryan G. Lim,Jennifer Stocksdale,Malcolm Casale,Terri G. Thompson,Jie Wu,Leslie M. Thompson,Victoria Dardov,Vidya Venkatraman,Andrea Matlock,Jennifer E. Van Eyk,Jacob D. Jaffe,Malvina Papanastasiou,Aravind Subramanian,Todd R. Golub,Sean D. Erickson,Mohammad Fallahi-Sichani,Marc Hafner,Nathanael S. Gray,Jia-Ren Lin,Caitlin E. Mills,Jeremy L. Muhlich,Mario Niepel,Caroline E. Shamu,Elizabeth H. Williams,David Wrobel,Peter K. Sorger,Laura M. Heiser,Joe W. Gray,James E. Korkola,Gordon B. Mills,Mark A. LaBarge,Mark A. LaBarge,Heidi S. Feiler,Mark A. Dane,Elmar Bucher,Michel Nederlof,Damir Sudar,Sean M. Gross,David Kilburn,Rebecca Smith,Kaylyn Devlin,Ron Margolis,Leslie Derr,Albert Lee,Ajay Pillai +107 more
TL;DR: The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders.
Journal ArticleDOI
MicroRNA-494 Targeting Both Proapoptotic and Antiapoptotic Proteins Protects Against Ischemia/Reperfusion-Induced Cardiac Injury
Xiaohong Wang,Xiaowei Zhang,Xiaoping Ren,Jing Chen,Hongzhu Liu,Jun-Qi Yang,Mario Medvedovic,Zhuowei Hu,Guo-Chang Fan +8 more
TL;DR: The findings suggest that although miR-494 targets both proapoptotic and antiap optotic proteins, the ultimate consequence is activation of the Akt pathway, leading to cardioprotective effects against I/R-induced injury.
Journal ArticleDOI
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Maureen A. Sartor,Craig R. Tomlinson,Scott C. Wesselkamper,Siva Sivaganesan,George D. Leikauf,Mario Medvedovic,Mario Medvedovic +6 more
TL;DR: A Bayesian hierarchical normal model is used to define a novel Intensity-Based Moderated T-statistic (IBMT), which is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters.