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Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Overview of attention for article published in BMC Cancer, August 2017
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Title
Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization
Published in
BMC Cancer, August 2017
DOI 10.1186/s12885-017-3500-5
Pubmed ID
Authors

Lin Wang, Xiaozhong Li, Louxin Zhang, Qiang Gao

Abstract

Human cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. Recently there are already some large panels of several hundred human cancer cell lines which are characterized with genomic and pharmacological data. The ability to predict drug responses using these pharmacogenomics data can facilitate the development of precision cancer medicines. Although several methods have been developed to address the drug response prediction, there are many challenges in obtaining accurate prediction. Based on the fact that similar cell lines and similar drugs exhibit similar drug responses, we adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using chemical structures of drugs and baseline gene expression levels in cell lines. Specifically, chemical structural similarity of drugs and gene expression profile similarity of cell lines were considered as regularization terms, which were incorporated to the drug response matrix factorization model. We first demonstrated the effectiveness of SRMF using a set of simulation data and compared it with two typical similarity-based methods. Furthermore, we applied it to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets, and performance of SRMF exceeds three state-of-the-art methods. We also applied SRMF to estimate the missing drug response values in the GDSC dataset. Even though SRMF does not specifically model mutation information, it could correctly predict drug-cancer gene associations that are consistent with existing data, and identify novel drug-cancer gene associations that are not found in existing data as well. SRMF can also aid in drug repositioning. The newly predicted drug responses of GDSC dataset suggest that mTOR inhibitor rapamycin was sensitive to non-small cell lung cancer (NSCLC), and expression of AK1RC3 and HINT1 may be adjunct markers of cell line sensitivity to rapamycin. Our analysis showed that the proposed data integration method is able to improve the accuracy of prediction of anticancer drug responses in cell lines, and can identify consistent and novel drug-cancer gene associations compared to existing data as well as aid in drug repositioning.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 29%
Student > Master 8 10%
Student > Bachelor 8 10%
Researcher 6 8%
Student > Doctoral Student 4 5%
Other 14 18%
Unknown 15 19%
Readers by discipline Count As %
Computer Science 20 26%
Biochemistry, Genetics and Molecular Biology 15 19%
Agricultural and Biological Sciences 8 10%
Medicine and Dentistry 5 6%
Engineering 4 5%
Other 7 9%
Unknown 18 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 16 May 2019.
All research outputs
#8,776,489
of 15,045,842 outputs
Outputs from BMC Cancer
#2,235
of 5,667 outputs
Outputs of similar age
#135,355
of 271,099 outputs
Outputs of similar age from BMC Cancer
#1
of 1 outputs
Altmetric has tracked 15,045,842 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,667 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 271,099 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
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