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Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Overview of attention for article published in BMC Cancer, May 2018
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Title
Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression
Published in
BMC Cancer, May 2018
DOI 10.1186/s12885-018-4483-6
Pubmed ID
Authors

Xinyan Zhang, Bingzong Li, Huiying Han, Sha Song, Hongxia Xu, Yating Hong, Nengjun Yi, Wenzhuo Zhuang

Abstract

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

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Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 38%
Student > Ph. D. Student 2 25%
Student > Bachelor 1 13%
Student > Doctoral Student 1 13%
Librarian 1 13%
Other 0 0%
Readers by discipline Count As %
Medicine and Dentistry 3 38%
Biochemistry, Genetics and Molecular Biology 3 38%
Computer Science 1 13%
Mathematics 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2018.
All research outputs
#10,621,621
of 13,343,384 outputs
Outputs from BMC Cancer
#3,310
of 5,002 outputs
Outputs of similar age
#200,146
of 267,887 outputs
Outputs of similar age from BMC Cancer
#1
of 1 outputs
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