J
Joseph S. Verducci
Researcher at Ohio State University
Publications - 48
Citations - 2698
Joseph S. Verducci is an academic researcher from Ohio State University. The author has contributed to research in topics: Ranking & Estimator. The author has an hindex of 21, co-authored 48 publications receiving 2552 citations.
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Journal ArticleDOI
MicroRNAs modulate the chemosensitivity of tumor cells
Paul E. Blower,Ji Hyun Chung,Joseph S. Verducci,Shili Lin,Jong Kook Park,Zunyan Dai,Chang Gong Liu,Thomas D. Schmittgen,William C. Reinhold,Carlo M. Croce,John N. Weinstein,Wolfgang Sadee +11 more
TL;DR: A substantial role for microRNAs in anticancer drug response is supported, suggesting novel potential approaches to the improvement of chemotherapy and comparing drug potencies with microRNA expression profiles across the entire NCI-60 panel.
Journal ArticleDOI
MICE Models: Superior to the HERG Model in Predicting Torsade de Pointes
James Kramer,Carlos A. Obejero-Paz,Glenn J. Myatt,Yuri A. Kuryshev,Andrew Bruening-Wright,Joseph S. Verducci,Arthur Brown +6 more
TL;DR: To test whether assaying concomitant block of multiple ion channels (Multiple Ion Channel Effects or MICE) improves predictivity, automated gigaseal patch clamp instruments were used to provide higher throughput along with accuracy and reproducibility and logistic regression models showed a significant reduction in false positives and false negatives.
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MicroRNA expression profiles for the NCI-60 cancer cell panel
Paul E. Blower,Joseph S. Verducci,Shili Lin,Jin Zhou,Ji Hyun Chung,Zunyan Dai,Chang Gong Liu,William C. Reinhold,Philip L. Lorenzi,Eric P. Kaldjian,Carlo M. Croce,John N. Weinstein,Wolfgang Sadee +12 more
TL;DR: Measureting expression levels of microRNAs in the NCI-60 and incorporating the resulting data into the CellMiner program package for integrative analysis found that there does not seem to be a significant correlation between microRNA expression patterns and those of known target transcripts.
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Multistage Ranking Models
TL;DR: In this article, a sample of people independently examines a fixed set of k items and then ranks these items according to personal judgment The process of ranking the items is decomposed into k − 1 stages in the forward model, and the best of the remaining items is selected at the second stage, and so on until the least preferred item is selected by default.
Journal ArticleDOI
Probability models on rankings.
TL;DR: In this paper, the authors investigate several probability models on permutations that have been proposed in the statistical and psychological literature and classify them into the following general classes: (1) Thurstone order statistics models, (2) ranking models induced by paired comparisons, (3) ranking based on distances between permutations, and (4) multistage ranking models.